Distributed Aggregation Protocol for Privacy Preserving Measurement
draft-ietf-ppm-dap-08
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| Authors | Tim Geoghegan , Christopher Patton , Eric Rescorla , Christopher A. Wood | ||
| Last updated | 2023-10-23 (Latest revision 2023-09-14) | ||
| Replaces | draft-gpew-priv-ppm | ||
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draft-ietf-ppm-dap-08
Network Working Group T. Geoghegan
Internet-Draft ISRG
Intended status: Standards Track C. Patton
Expires: 25 April 2024 Cloudflare
E. Rescorla
Mozilla
C. A. Wood
Cloudflare
23 October 2023
Distributed Aggregation Protocol for Privacy Preserving Measurement
draft-ietf-ppm-dap-08
Abstract
There are many situations in which it is desirable to take
measurements of data which people consider sensitive. In these
cases, the entity taking the measurement is usually not interested in
people's individual responses but rather in aggregated data.
Conventional methods require collecting individual responses and then
aggregating them, thus representing a threat to user privacy and
rendering many such measurements difficult and impractical. This
document describes a multi-party distributed aggregation protocol
(DAP) for privacy preserving measurement (PPM) which can be used to
collect aggregate data without revealing any individual user's data.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at https://ietf-wg-
ppm.github.io/draft-ietf-ppm-dap/draft-ietf-ppm-dap.html. Status
information for this document may be found at
https://datatracker.ietf.org/doc/draft-ietf-ppm-dap/.
Discussion of this document takes place on the Privacy Preserving
Measurement Working Group mailing list (mailto:ppm@ietf.org), which
is archived at https://mailarchive.ietf.org/arch/browse/ppm/.
Subscribe at https://www.ietf.org/mailman/listinfo/ppm/.
Source for this draft and an issue tracker can be found at
https://github.com/ietf-wg-ppm/draft-ietf-ppm-dap.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 25 April 2024.
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Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1. Change Log . . . . . . . . . . . . . . . . . . . . . . . 4
1.2. Conventions and Definitions . . . . . . . . . . . . . . . 7
2. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1. System Architecture . . . . . . . . . . . . . . . . . . . 10
2.2. Validating Inputs . . . . . . . . . . . . . . . . . . . . 12
3. Message Transport . . . . . . . . . . . . . . . . . . . . . . 13
3.1. HTTPS Request Authentication . . . . . . . . . . . . . . 13
3.2. Errors . . . . . . . . . . . . . . . . . . . . . . . . . 13
4. Protocol Definition . . . . . . . . . . . . . . . . . . . . . 15
4.1. Queries . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1.1. Time-interval Queries . . . . . . . . . . . . . . . . 18
4.1.2. Fixed-size Queries . . . . . . . . . . . . . . . . . 18
4.2. Task Configuration . . . . . . . . . . . . . . . . . . . 19
4.3. Resource URIs . . . . . . . . . . . . . . . . . . . . . . 20
4.4. Uploading Reports . . . . . . . . . . . . . . . . . . . . 21
4.4.1. HPKE Configuration Request . . . . . . . . . . . . . 21
4.4.2. Upload Request . . . . . . . . . . . . . . . . . . . 23
4.4.3. Upload Extensions . . . . . . . . . . . . . . . . . . 26
4.5. Verifying and Aggregating Reports . . . . . . . . . . . . 26
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4.5.1. Aggregate Initialization . . . . . . . . . . . . . . 29
4.5.2. Aggregate Continuation . . . . . . . . . . . . . . . 39
4.6. Collecting Results . . . . . . . . . . . . . . . . . . . 43
4.6.1. Collection Job Initialization . . . . . . . . . . . . 44
4.6.2. Obtaining Aggregate Shares . . . . . . . . . . . . . 47
4.6.3. Collection Job Finalization . . . . . . . . . . . . . 49
4.6.4. Aggregate Share Encryption . . . . . . . . . . . . . 50
4.6.5. Batch Validation . . . . . . . . . . . . . . . . . . 51
5. Operational Considerations . . . . . . . . . . . . . . . . . 53
5.1. Protocol participant capabilities . . . . . . . . . . . . 53
5.1.1. Client capabilities . . . . . . . . . . . . . . . . . 53
5.1.2. Aggregator capabilities . . . . . . . . . . . . . . . 53
5.1.3. Collector capabilities . . . . . . . . . . . . . . . 54
5.2. Data resolution limitations . . . . . . . . . . . . . . . 54
5.3. Aggregation utility and soft batch deadlines . . . . . . 55
5.4. Protocol-specific optimizations . . . . . . . . . . . . . 55
5.4.1. Reducing storage requirements . . . . . . . . . . . . 55
6. Compliance Requirements . . . . . . . . . . . . . . . . . . . 56
7. Security Considerations . . . . . . . . . . . . . . . . . . . 56
7.1. Threat model . . . . . . . . . . . . . . . . . . . . . . 57
7.1.1. Client/user . . . . . . . . . . . . . . . . . . . . . 57
7.1.2. Aggregator . . . . . . . . . . . . . . . . . . . . . 58
7.1.3. Leader . . . . . . . . . . . . . . . . . . . . . . . 60
7.1.4. Aggregator collusion . . . . . . . . . . . . . . . . 60
7.1.5. Attacker on the network . . . . . . . . . . . . . . . 61
7.2. Sybil attacks . . . . . . . . . . . . . . . . . . . . . . 62
7.2.1. Client authentication . . . . . . . . . . . . . . . . 62
7.3. Anonymizing proxies . . . . . . . . . . . . . . . . . . . 63
7.4. Task parameters . . . . . . . . . . . . . . . . . . . . . 63
7.4.1. Verification key requirements . . . . . . . . . . . . 63
7.4.2. Batch parameters . . . . . . . . . . . . . . . . . . 64
7.4.3. VDAFs and compute requirements . . . . . . . . . . . 64
7.5. Differential privacy . . . . . . . . . . . . . . . . . . 65
7.6. Robustness in the presence of malicious servers . . . . . 65
7.7. Infrastructure diversity . . . . . . . . . . . . . . . . 65
7.8. System requirements . . . . . . . . . . . . . . . . . . . 65
7.8.1. Data types . . . . . . . . . . . . . . . . . . . . . 65
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 65
8.1. Protocol Message Media Types . . . . . . . . . . . . . . 65
8.1.1. "application/dap-hpke-config-list" media type . . . . 66
8.1.2. "application/dap-report" media type . . . . . . . . . 67
8.1.3. "application/dap-aggregation-job-init-req" media
type . . . . . . . . . . . . . . . . . . . . . . . . 68
8.1.4. "application/dap-aggregation-job-resp" media type . . 69
8.1.5. "application/dap-aggregation-job-continue-req" media
type . . . . . . . . . . . . . . . . . . . . . . . . 70
8.1.6. "application/dap-aggregate-share-req" media type . . 71
8.1.7. "application/dap-aggregate-share" media type . . . . 71
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8.1.8. "application/dap-collect-req" media type . . . . . . 72
8.1.9. "application/dap-collection" media type . . . . . . . 73
8.2. DAP Type Registries . . . . . . . . . . . . . . . . . . . 74
8.2.1. Query Types Registry . . . . . . . . . . . . . . . . 74
8.2.2. Upload Extension Registry . . . . . . . . . . . . . . 74
8.2.3. Prepare Error Registry . . . . . . . . . . . . . . . 74
8.3. URN Sub-namespace for DAP (urn:ietf:params:ppm:dap) . . . 75
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 75
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 75
10.1. Normative References . . . . . . . . . . . . . . . . . . 75
10.2. Informative References . . . . . . . . . . . . . . . . . 77
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 78
1. Introduction
This document describes the Distributed Aggregation Protocol (DAP)
for privacy preserving measurement. The protocol is executed by a
large set of clients and a small set of servers. The servers' goal
is to compute some aggregate statistic over the clients' inputs
without learning the inputs themselves. This is made possible by
distributing the computation among the servers in such a way that, as
long as at least one of them executes the protocol honestly, no input
is ever seen in the clear by any server.
1.1. Change Log
(*) Indicates a change that breaks wire compatibility with the
previous draft.
08:
* Clarify requirements for initializing aggregation jobs.
* Add more considerations for Sybil attacks.
* Expand guidance around choosing the VDAF verification key.
* Add an error type registry for the aggregation sub-protocol.
07:
* Bump version tag from "dap-06" to "dap-07". This is a bug-fix
revision: the editors overlooked some changes we intended to pick
up in the previous version. (*)
06:
* Bump draft-irtf-cfrg-vdaf-06 to 07 [VDAF]. (*)
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* Overhaul security considerations (#488).
* Adopt revised ping-pong interface in draft-irtf-cfrg-vdaf-07
(#494).
* Add aggregation parameter to AggregateShareAad (#498). (*)
* Bump version tag from "dap-05" to "dap-06". (*)
05:
* Bump draft-irtf-cfrg-vdaf-05 to 06 [VDAF]. (*)
* Specialize the protocol for two-party VDAFs (i.e., one Leader and
One Helper). Accordingly, update the aggregation sub-protocol to
use the new "ping-pong" interface for two-party VDAFs introduced
in draft-irtf-cfrg-vdaf-06. (*)
* Allow the following actions to be safely retried: aggregation job
creation, collection job creation, and requesting the Helper's
aggregate share.
* Merge error types that are related.
* Drop recommendation to generate IDs using a cryptographically
secure pseudorandom number generator wherever pseudorandomness is
not required.
* Require HPKE config identifiers to be unique.
* Bump version tag from "dap-04" to "dap-05". (*)
04:
* Introduce resource oriented HTTP API. (#278, #398, #400) (*)
* Clarify security requirements for choosing VDAF verify key. (#407,
#411)
* Require Clients to provide nonce and random input when sharding
inputs. (#394, #425) (*)
* Add interval of time spanned by constituent reports to Collection
message. (#397, #403) (*)
* Update share validation requirements based on latest security
analysis. (#408, #410)
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* Bump draft-irtf-cfrg-vdaf-03 to 05 [VDAF]. (#429) (*)
* Bump version tag from "dap-03" to "dap-04". (#424) (*)
03:
* Enrich the "fixed_size" query type to allow the Collector to
request a recently aggregated batch without knowing the batch ID
in advance. ID discovery was previously done out-of-band. (*)
* Allow Aggregators to advertise multiple HPKE configurations. (*)
* Clarify requirements for enforcing anti-replay. Namely, while it
is sufficient to detect repeated report IDs, it is also enough to
detect repeated IDs and timestamps.
* Remove the extensions from the Report and add extensions to the
plaintext payload of each ReportShare. (*)
* Clarify that extensions are mandatory to implement: If an
Aggregator does not recognize a ReportShare's extension, it must
reject it.
* Clarify that Aggregators must reject any ReportShare with repeated
extension types.
* Specify explicitly how to serialize the Additional Authenticated
Data (AAD) string for HPKE encryption. This clarifies an
ambiguity in the previous version. (*)
* Change the length tag for the aggregation parameter to 32 bits.
(*)
* Use the same prefix ("application") for all media types. (*)
* Make input share validation more explicit, including adding a new
ReportShareError variant, "report_too_early", for handling reports
too far in the future. (*)
* Improve alignment of problem details usage with [RFC7807].
Replace "reportTooLate" problem document type with
"repjortRejected" and clarify handling of rejected reports in the
upload sub-protocol. (*)
* Bump version tag from "dap-02" to "dap-03". (*)
02:
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* Define a new task configuration parameter, called the "query
type", that allows tasks to partition reports into batches in
different ways. In the current draft, the Collector specifies a
"query", which the Aggregators use to guide selection of the
batch. Two query types are defined: the "time_interval" type
captures the semantics of draft 01; and the "fixed_size" type
allows the Leader to partition the reports arbitrarily, subject to
the constraint that each batch is roughly the same size. (*)
* Define a new task configuration parameter, called the task
"expiration", that defines the lifetime of a given task.
* Specify requirements for HTTP request authentication rather than a
concrete scheme. (Draft 01 required the use of the DAP-Auth-Token
header; this is now optional.)
* Make "task_id" an optional parameter of the "/hpke_config"
endpoint.
* Add report count to CollectResp message. (*)
* Increase message payload sizes to accommodate VDAFs with input and
aggregate shares larger than 2^16-1 bytes. (*)
* Bump draft-irtf-cfrg-vdaf-01 to 03 [VDAF]. (*)
* Bump version tag from "dap-01" to "dap-02". (*)
* Rename the report nonce to the "report ID" and move it to the top
of the structure. (*)
* Clarify when it is safe for an Aggregator to evict various data
artifacts from long-term storage.
1.2. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
Aggregate result: The output of the aggregation function computed
over a batch of measurements and an aggregation parameter. As
defined in [VDAF].
Aggregate share: A share of the aggregate result emitted by an
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Aggregator. Aggregate shares are reassembled by the Collector
into the aggregate result, which is the final output of the
aggregation function. As defined in [VDAF].
Aggregation function: The function computed over the Clients'
measurements. As defined in [VDAF].
Aggregation parameter: Parameter used to prepare a set of
measurements for aggregation (e.g., the candidate prefixes for
Poplar1 from Section 8 of [VDAF]). As defined in [VDAF].
Aggregator: An endpoint that receives input shares from Clients and
validates and aggregates them with the help of the other
Aggregators.
Batch: A set of reports (i.e., measurements) that are aggregated
into an aggregate result.
Batch duration: The time difference between the oldest and newest
report in a batch.
Batch interval: A parameter of a query issued by the Collector that
specifies the time range of the reports in the batch.
Client: A party that uploads a report.
Collector: The endpoint that selects the aggregation parameter and
receives the aggregate result.
Helper: The Aggregator that executes the aggregation and collection
sub-protocols as instructed by the Leader.
Input share: An Aggregator's share of a measurement. The input
shares are output by the VDAF sharding algorithm. As defined in
[VDAF].
Output share: An Aggregator's share of the refined measurement
resulting from successful execution of the VDAF preparation phase.
Many output shares are combined into an aggregate share during the
VDAF aggregation phase. As defined in [VDAF].
Leader: The Aggregator that coordinates aggregation and collection
with the Helper.
Measurement: A plaintext input emitted by a Client (e.g., a count,
summand, or string), before any encryption or secret sharing is
applied. Depending on the VDAF in use, multiple values may be
grouped into a single measurement. As defined in [VDAF].
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Minimum batch size: The minimum number of reports in a batch.
Public share: The output of the VDAF sharding algorithm broadcast to
each of the Aggregators. As defined in [VDAF].
Report: A cryptographically protected measurement uploaded to the
Leader by a Client. Comprised of a set of report shares.
Report Share: An encrypted input share comprising a piece of a
report.
This document uses the presentation language of [RFC8446] to define
messages in the DAP protocol. Encoding and decoding of these
messages as byte strings also follows [RFC8446].
2. Overview
The protocol is executed by a large set of Clients and a pair of
servers referred to as "Aggregators". Each Client's input to the
protocol is its measurement (or set of measurements, e.g., counts of
some user behavior). Given the input set of measurements x_1, ...,
x_n held by n Clients, and an aggregation parameter p shared by the
Aggregators, the goal of DAP is to compute y = F(p, x_1, ..., x_n)
for some function F while revealing nothing else about the
measurements. We call F the "aggregation function."
This protocol is extensible and allows for the addition of new
cryptographic schemes that implement the VDAF interface specified in
[VDAF]. Candidates include:
* Prio3 (Section 7 of [VDAF]), which allows for aggregate statistics
such as sum, mean, histograms, etc.
* Poplar1 (Section 8 of [VDAF]), which allows for finding the most
popular strings uploaded by a set of Clients (e.g., the URL of
their home page) as well as counting the number of Clients that
hold a given string. This VDAF is the basis of the Poplar
protocol of [BBCGGI21], which is designed to solve the heavy
hitters problem in a privacy preserving manner.
VDAFs rely on secret sharing to protect the privacy of the
measurements. Rather than sending its input in the clear, each
Client shards its measurement into a pair of "input shares" and sends
an input share to each of the Aggregators. This provides two
important properties:
* Given only one of the input shares, it is impossible to deduce the
plaintext measurement from which it was generated.
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* It allows the Aggregators to compute the aggregation function by
first aggregating up their input shares locally into "aggregate
shares", then combining the aggregate shares into the aggregate
result.
2.1. System Architecture
The overall system architecture is shown in Figure 1.
+--------+
| |
| Client +----+
| | |
+--------+ |
|
+--------+ | +------------+ +-----------+
| | +-----> | | |
| Client +----------> Leader <---------> Collector |
| | +-----> | | |
+--------+ | +-----^------+ +-----------+
| |
+--------+ | |
| | | |
| Client +----+ +-----V------+
| | | |
+--------+ | Helper |
| |
+------------+
Figure 1: System Architecture
The main participants in the protocol are as follows:
Collector: The entity which wants to obtain the aggregate of the
measurements generated by the Clients. Any given measurement task
will have a single Collector.
Client(s): The endpoints which directly take the measurement(s) and
report them to the DAP protocol. In order to provide reasonable
levels of privacy, there must be a large number of Clients.
Aggregator: An endpoint which receives report shares. Each
Aggregator works with its co-Aggregator to compute the aggregate
result. Any given measurement task will have two Aggregators: a
Leader and a Helper.
Leader: The Aggregator responsible for coordinating the protocol.
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It receives the reports, splits them into report shares,
distributes the report shares to the Helper, and orchestrates the
process of computing the aggregate result as requested by the
Collector.
Helper: The Aggregator assisting the Leader with the computation.
The protocol is designed so that the Helper is relatively
lightweight, with most of the operational burdern born by the
Leader.
The basic unit of DAP is the "task" which represents a single
measurement process (though potentially aggregating multiple batches
of measurements). The definition of a task includes the following
parameters:
* The type of each measurement.
* The aggregation function to compute (e.g., sum, mean, etc.).
* The set of Aggregators and necessary cryptographic keying material
to use.
* The VDAF to execute, which to some extent is dictated by the
previous choices.
* The minimum "batch size" of reports which can be aggregated.
* The rate at which measurements can be taken, i.e., the "minimum
batch duration".
These parameters are distributed to the Clients, Aggregators, and
Collector before the task begins. This document does not specify a
distribution mechanism, but it is important that all protocol
participants agree on the task's configuration. Each task is
identified by a unique 32-byte ID which is used to refer to it in
protocol messages.
During the lifetime of a task, each Client records its own
measurement value(s), packages them up into a report, and sends them
to the Leader. Each share is separately encrypted for each
Aggregator so that even though they pass through the Leader, the
Leader is unable to see or modify them. Depending on the task, the
Client may only send one report or may send many reports over time.
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The Leader distributes the shares to the Helper and orchestrates the
process of verifying them (see Section 2.2) and assembling them into
a final aggregate result for the Collector. Depending on the VDAF,
it may be possible to incrementally process each report as it comes
in, or may be necessary to wait until the entire batch of reports is
received.
2.2. Validating Inputs
An essential task of any data collection pipeline is ensuring that
the data being aggregated is "valid". In DAP, input validation is
complicated by the fact that none of the entities other than the
Client ever sees that Client's plaintext measurement.
In order to address this problem, the Aggregators engage in a secure,
multi-party computation specified by the chosen VDAF [VDAF] in order
to prepare a report for aggregation. At the beginning of this
computation, each Aggregator is in possession of an input share
uploaded by the Client. At the end of the computation, each
Aggregator is in possession of either an "output share" that is ready
to be aggregated or an indication that a valid output share could not
be computed.
To facilitate this computation, the input shares generated by the
Client include information used by the Aggregators during aggregation
in order to validate their corresponding output shares. For example,
Prio3 includes a zero-knowledge proof of the input's validity (see
Section 7.1 of [VDAF]). which the Aggregators can jointly verify and
reject the report if it cannot be verified. However, they do not
learn anything about the individual report other than that it is
valid.
The specific properties attested to in the proof vary depending on
the measurement being taken. For instance, to measure the time the
user took performing a given task the proof might demonstrate that
the value reported was within a certain range (e.g., 0-60 seconds).
By contrast, to report which of a set of N options the user select,
the report might contain N integers and the proof would demonstrate
that N-1 were 0 and the other was 1.
It is important to recognize that "validity" is distinct from
"correctness". For instance, the user might have spent 30s on a task
but the Client might report 60s. This is a problem with any
measurement system and DAP does not attempt to address it; it merely
ensures that the data is within acceptable limits, so the Client
could not report 10^6s or -20s.
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3. Message Transport
Communications between DAP participants are carried over HTTPS
[RFC9110]. HTTPS provides server authentication and confidentiality.
Use of HTTPS is REQUIRED.
3.1. HTTPS Request Authentication
DAP is made up of several sub-protocols in which different subsets of
the protocol's participants interact with each other.
In those cases where a channel between two participants is tunneled
through another protocol participant, DAP mandates the use of public-
key encryption using [HPKE] to ensure that only the intended
recipient can see a message in the clear.
In other cases, DAP requires HTTPS client authentication as well as
server authentication. Any authentication scheme that is composable
with HTTP is allowed. For example:
* [OAuth2] credentials are presented in an Authorization HTTP
header, which can be added to any DAP protocol message.
* TLS client certificates can be used to authenticate the underlying
transport.
* The DAP-Auth-Token HTTP header described in
[I-D.draft-dcook-ppm-dap-interop-test-design-04].
This flexibility allows organizations deploying DAP to use existing
well-known HTTP authentication mechanisms that they already support.
Discovering what authentication mechanisms are supported by a DAP
participant is outside of this document's scope.
3.2. Errors
Errors can be reported in DAP both at the HTTP layer and within
challenge objects as defined in Section 8. DAP servers can return
responses with an HTTP error response code (4XX or 5XX). For
example, if the Client submits a request using a method not allowed
in this document, then the server MAY return HTTP status code 405
Method Not Allowed.
When the server responds with an error status, it SHOULD provide
additional information using a problem detail object [RFC9457]. To
facilitate automatic response to errors, this document defines the
following standard tokens for use in the "type" field (within the DAP
URN namespace "urn:ietf:params:ppm:dap:error:"):
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+============================+=====================================+
| Type | Description |
+============================+=====================================+
| invalidMessage | A message received by a protocol |
| | participant could not be parsed or |
| | otherwise was invalid. |
+----------------------------+-------------------------------------+
| unrecognizedTask | An endpoint received a message with |
| | an unknown task ID. |
+----------------------------+-------------------------------------+
| unrecognizedAggregationJob | An endpoint received a message with |
| | an unknown aggregation job ID. |
+----------------------------+-------------------------------------+
| outdatedConfig | The message was generated using an |
| | outdated configuration. |
+----------------------------+-------------------------------------+
| reportRejected | Report could not be processed for |
| | an unspecified reason. |
+----------------------------+-------------------------------------+
| reportTooEarly | Report could not be processed |
| | because its timestamp is too far in |
| | the future. |
+----------------------------+-------------------------------------+
| batchInvalid | The batch boundary check for |
| | Collector's query failed. |
+----------------------------+-------------------------------------+
| invalidBatchSize | There are an invalid number of |
| | reports in the batch. |
+----------------------------+-------------------------------------+
| batchQueriedTooManyTimes | The maximum number of batch queries |
| | has been exceeded for one or more |
| | reports included in the batch. |
+----------------------------+-------------------------------------+
| batchMismatch | Aggregators disagree on the report |
| | shares that were aggregated in a |
| | batch. |
+----------------------------+-------------------------------------+
| unauthorizedRequest | Authentication of an HTTP request |
| | failed (see Section 3.1). |
+----------------------------+-------------------------------------+
| missingTaskID | HPKE configuration was requested |
| | without specifying a task ID. |
+----------------------------+-------------------------------------+
| stepMismatch | The Aggregators disagree on the |
| | current step of the DAP aggregation |
| | protocol. |
+----------------------------+-------------------------------------+
| batchOverlap | A request's query includes reports |
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| | that were previously collected in a |
| | different batch. |
+----------------------------+-------------------------------------+
Table 1
This list is not exhaustive. The server MAY return errors set to a
URI other than those defined above. Servers MUST NOT use the DAP URN
namespace for errors not listed in the appropriate IANA registry (see
Section 8.3). The "detail" member of the Problem Details document
includes additional diagnostic information.
When the task ID is known (see Section 4.2), the problem document
SHOULD include an additional "taskid" member containing the ID
encoded in Base 64 using the URL and filename safe alphabet with no
padding defined in Sections 5 and 3.2 of [RFC4648].
In the remainder of this document, the tokens in the table above are
used to refer to error types, rather than the full URNs. For
example, an "error of type 'invalidMessage'" refers to an error
document with "type" value
"urn:ietf:params:ppm:dap:error:invalidMessage".
This document uses the verbs "abort" and "alert with [some error
message]" to describe how protocol participants react to various
error conditions. This implies HTTP status code 400 Bad Request
unless explicitly specified otherwise.
4. Protocol Definition
DAP has three major interactions which need to be defined:
* Uploading reports from the Client to the Aggregators, specified in
Section 4.4
* Computing the results for a given measurement task, specified in
Section 4.5
* Collecting aggregated results, specified in Section 4.6
Each of these interactions is defined in terms of "resources". In
this section we define these resources and the messages used to act
on them.
The following are some basic type definitions used in other messages:
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/* ASCII encoded URL. e.g., "https://example.com" */
opaque Url<1..2^16-1>;
uint64 Duration; /* Number of seconds elapsed between two instants */
uint64 Time; /* seconds elapsed since start of UNIX epoch */
/* An interval of time of length duration, where start is included and (start +
duration) is excluded. */
struct {
Time start;
Duration duration;
} Interval;
/* An ID used to uniquely identify a report in the context of a DAP task. */
opaque ReportID[16];
/* The various roles in the DAP protocol. */
enum {
collector(0),
client(1),
leader(2),
helper(3),
(255)
} Role;
/* Identifier for a server's HPKE configuration */
uint8 HpkeConfigId;
/* An HPKE ciphertext. */
struct {
HpkeConfigId config_id; /* config ID */
opaque enc<1..2^16-1>; /* encapsulated HPKE key */
opaque payload<1..2^32-1>; /* ciphertext */
} HpkeCiphertext;
/* Represent a zero-length byte string. */
struct {} Empty;
DAP uses the 16-byte ReportID as the nonce parameter for the VDAF
shard and prep_init methods (see [VDAF], Section 5). Thus for a VDAF
to be compatible with DAP, it MUST specify a NONCE_SIZE of 16 bytes.
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4.1. Queries
Aggregated results are computed based on sets of reports, called
"batches". The Collector influences which reports are used in a
batch via a "query." The Aggregators use this query to carry out the
aggregation flow and produce aggregate shares encrypted to the
Collector.
This document defines the following query types:
enum {
reserved(0), /* Reserved for testing purposes */
time_interval(1),
fixed_size(2),
(255)
} QueryType;
The time_interval query type is described in Section 4.1.1; the
fixed_size query type is described in Section 4.1.2. Future
specifications may introduce new query types as needed (see
Section 8.2.1). A query includes parameters used by the Aggregators
to select a batch of reports specific to the given query type. A
query is defined as follows:
opaque BatchID[32];
enum {
by_batch_id(0),
current_batch(1),
} FixedSizeQueryType;
struct {
FixedSizeQueryType query_type;
select (FixedSizeQuery.query_type) {
by_batch_id: BatchID batch_id;
current_batch: Empty;
}
} FixedSizeQuery;
struct {
QueryType query_type;
select (Query.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: FixedSizeQuery fixed_size_query;
}
} Query;
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The query is issued in-band as part of the collect sub-protocol
(Section 4.6). Its content is determined by the "query type", which
in turn is encoded by the "query configuration" configured out-of-
band. All query types have the following configuration parameters in
common:
* min_batch_size - The smallest number of reports the batch is
allowed to include. In a sense, this parameter controls the
degree of privacy that will be obtained: the larger the minimum
batch size, the higher degree of privacy. However, this
ultimately depends on the application and the nature of the
measurements and aggregation function.
* time_precision - Clients use this value to truncate their report
timestamps; see Section 4.4. Additional semantics may apply,
depending on the query type. (See Section 4.6.5 for details.)
The parameters pertaining to specific query types are described in
the relevant subsection below.
4.1.1. Time-interval Queries
The first query type, time_interval, is designed to support
applications in which reports are collected over a long period of
time. The Collector specifies a "batch interval" that determines the
time range for reports included in the batch. For each report in the
batch, the time at which that report was generated (see Section 4.4)
MUST fall within the batch interval specified by the Collector.
Typically the Collector issues queries for which the batch intervals
are continuous, monotonically increasing, and have the same duration.
For example, the sequence of batch intervals (1659544000, 1000),
(1659545000, 1000), (1659546000, 1000), (1659547000, 1000) satisfies
these conditions. (The first element of the pair denotes the start
of the batch interval and the second denotes the duration.) Of
course, there are cases in which Collector may need to issue queries
out-of-order. For example, a previous batch might need to be queried
again with a different aggregation parameter (e.g, for Poplar1). In
addition, the Collector may need to vary the duration to adjust to
changing report upload rates.
4.1.2. Fixed-size Queries
The fixed_size query type is used to support applications in which
the Collector needs the ability to strictly control the sample size.
This is particularly important for controlling the amount of noise
added to reports by Clients (or added to aggregate shares by
Aggregators) in order to achieve differential privacy.
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For this query type, the Aggregators group reports into arbitrary
batches such that each batch has roughly the same number of reports.
These batches are identified by opaque "batch IDs", allocated in an
arbitrary fashion by the Leader.
To get the aggregate of a batch, the Collector issues a query
specifying the batch ID of interest (see Section 4.1). The Collector
may not know which batch ID it is interested in; in this case, it can
also issue a query of type current_batch, which allows the Leader to
select a recent batch to aggregate. The Leader SHOULD select a batch
which has not yet began collection.
In addition to the minimum batch size common to all query types, the
configuration includes a parameter max_batch_size that determines
maximum number of reports per batch.
Implementation note: The goal for the Aggregators is to aggregate
precisely min_batch_size reports per batch. Doing so, however, may
be challenging for Leader deployments in which multiple, independent
nodes running the aggregate sub-protocol (see Section 4.5) need to be
coordinated. The maximum batch size is intended to allow room for
error. Typically the difference between the minimum and maximum
batch size will be a small fraction of the target batch size for each
batch.
[OPEN ISSUE: It may be feasible to require a fixed batch size, i.e.,
min_batch_size == max_batch_size. We should know better once we've
had some implementation/deployment experience.]
4.2. Task Configuration
Prior to the start of execution of the protocol, each participant
must agree on the configuration for each task. A task is uniquely
identified by its task ID:
opaque TaskID[32];
The task ID value MUST be a globally unique sequence of bytes. Each
task has the following parameters associated with it:
* leader_aggregator_endpoint: A URL relative to which the Leader's
API endpoints can be found.
* helper_aggregator_endpoint: A URL relative to which the Helper's
API endpoints can be found.
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* The query configuration for this task (see Section 4.1). This
determines the query type for batch selection and the properties
that all batches for this task must have.
* max_batch_query_count: The maximum number of times a batch of
reports may be queried by the Collector.
* task_expiration: The time up to which Clients are expected to
upload to this task. The task is considered completed after this
time. Aggregators MAY reject reports that have timestamps later
than task_expiration.
* A unique identifier for the VDAF in use for the task, e.g., one of
the VDAFs defined in Section 10 of [VDAF].
In addition, in order to facilitate the aggregation and collect
protocols, each of the Aggregators is configured with following
parameters:
* collector_hpke_config: The [HPKE] configuration of the Collector
(described in Section 4.4.1); see Section 6 for information about
the HPKE configuration algorithms.
* vdaf_verify_key: The VDAF verification key shared by the
Aggregators. This key is used in the aggregation sub-protocol
(Section 4.5). The security requirements are described in
Section 7.4.1.
Finally, the Collector is configured with the HPKE secret key
corresponding to collector_hpke_config.
A task's parameters are immutable for the lifetime of that task. The
only way to change parameters or to rotate secret values like
collector HPKE configuration or the VDAF verification key is to
configure a new task.
4.3. Resource URIs
DAP is defined in terms of "resources", such as reports
(Section 4.4), aggregation jobs (Section 4.5), and collection jobs
(Section 4.6). Each resource has an associated URI. Resource URIs
are specified by a sequence of string literals and variables.
Variables are expanded into strings according to the following rules:
* Variables {leader} and {helper} are replaced with the base URL of
the Leader and Helper respectively (the base URL is defined in
Section 4.2).
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* Variables {task-id}, {aggregation-job-id}, and {collection-job-id}
are replaced with the task ID (Section 4.2), aggregation job ID
(Section 4.5.1), and collection job ID (Section 4.6.1)
respectively. The value MUST be encoded in its URL-safe, unpadded
Base 64 representation as specified in Sections 5 and 3.2 of
[RFC4648].
For example, resource URI {leader}/tasks/{task-id}/reports might be
expanded into https://example.com/tasks/8BY0RzZMzxvA46_8ymhzycOB9krN-
QIGYvg_RsByGec/reports
4.4. Uploading Reports
Clients periodically upload reports to the Leader. Each report
contains two "report shares", one for the Leader and another for the
Helper. The Helper's report share is transmitted by the Leader
during the aggregation sub-protocol (see Section 4.5).
4.4.1. HPKE Configuration Request
Before the Client can upload its report to the Leader, it must know
the HPKE configuration of each Aggregator. See Section 6 for
information on HPKE algorithm choices.
Clients retrieve the HPKE configuration from each Aggregator by
sending an HTTP GET request to {aggregator}/hpke_config. Clients MAY
specify a query parameter task_id whose value is the task ID whose
HPKE configuration they want. If the Aggregator does not recognize
the task ID, then it MUST abort with error unrecognizedTask.
An Aggregator is free to use different HPKE configurations for each
task with which it is configured. If the task ID is missing from the
Client's request, the Aggregator MAY abort with an error of type
missingTaskID, in which case the Client SHOULD retry the request with
a well-formed task ID included.
An Aggregator responds to well-formed requests with HTTP status code
200 OK and an HpkeConfigList value, with media type "application/dap-
hpke-config-list". The HpkeConfigList structure contains one or more
HpkeConfig structures in decreasing order of preference. This allows
an Aggregator to support multiple HPKE configurations simultaneously.
[TODO: Allow Aggregators to return HTTP status code 403 Forbidden in
deployments that use authentication to avoid leaking information
about which tasks exist.]
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HpkeConfig HpkeConfigList<1..2^16-1>;
struct {
HpkeConfigId id;
HpkeKemId kem_id;
HpkeKdfId kdf_id;
HpkeAeadId aead_id;
HpkePublicKey public_key;
} HpkeConfig;
opaque HpkePublicKey<1..2^16-1>;
uint16 HpkeAeadId; /* Defined in [HPKE] */
uint16 HpkeKemId; /* Defined in [HPKE] */
uint16 HpkeKdfId; /* Defined in [HPKE] */
[OPEN ISSUE: Decide whether to expand the width of the id.]
Aggregators MUST allocate distinct id values for each HpkeConfig in
an HpkeConfigList.
The Client MUST abort if any of the following happen for any HPKE
config request:
* the GET request failed or did not return a valid HPKE config list;
* the HPKE config list is empty; or
* no HPKE config advertised by the Aggregator specifies a supported
a KEM, KDF, or AEAD algorithm triple.
Aggregators SHOULD use HTTP caching to permit client-side caching of
this resource [RFC5861]. Aggregators SHOULD favor long cache
lifetimes to avoid frequent cache revalidation, e.g., on the order of
days. Aggregators can control this cached lifetime with the Cache-
Control header, as follows:
Cache-Control: max-age=86400
Clients SHOULD follow the usual HTTP caching [RFC9111] semantics for
HPKE configurations.
Note: Long cache lifetimes may result in Clients using stale HPKE
configurations; Aggregators SHOULD continue to accept reports with
old keys for at least twice the cache lifetime in order to avoid
rejecting reports.
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4.4.2. Upload Request
Clients upload reports by using an HTTP PUT to {leader}/tasks/{task-
id}/reports. The payload is a Report, with media type "application/
dap-report", structured as follows:
struct {
ReportID report_id;
Time time;
} ReportMetadata;
struct {
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext leader_encrypted_input_share;
HpkeCiphertext helper_encrypted_input_share;
} Report;
* report_metadata is public metadata describing the report.
- report_id is used by the Aggregators to ensure the report
appears in at most one batch (see Section 4.5.1.4). The Client
MUST generate this by generating 16 random bytes using a
cryptographically secure random number generator.
- time is the time at which the report was generated. The Client
SHOULD round this value down to the nearest multiple of the
task's time_precision in order to ensure that that the
timestamp cannot be used to link a report back to the Client
that generated it.
* public_share is the public share output by the VDAF sharding
algorithm. Note that the public share might be empty, depending
on the VDAF.
* leader_encrypted_input_share is the Leader's encrypted input
share.
* helper_encrypted_input_share is the Helper's encrypted input
share.
Aggregators MAY require clients to authenticate when uploading
reports (see Section 7.2.1). If it is used, Client authentication
MUST use a scheme that meets the requirements in Section 3.1.
To generate a report, the Client begins by sharding its measurement
into input shares and the public share using the VDAF's sharding
algorithm (Section 5.1 of [VDAF]), using the report ID as the nonce:
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(public_share, input_shares) = Vdaf.shard(
measurement, /* plaintext measurement */
report_id, /* nonce */
rand, /* randomness for sharding algorithm */
)
The last input comprises the randomness consumed by the sharding
algorithm. The sharding randomness is a random byte string of length
specified by the VDAF. The Client MUST generate this using a
cryptographically secure random number generator.
The Client then wraps each input share in the following structure:
struct {
Extension extensions<0..2^16-1>;
opaque payload<0..2^32-1>;
} PlaintextInputShare;
Field extensions is set to the list of extensions intended to be
consumed by the given Aggregator. (See Section 4.4.3.) Field
payload is set to the Aggregator's input share output by the VDAF
sharding algorithm.
Next, the Client encrypts each PlaintextInputShare
plaintext_input_share as follows:
enc, payload = SealBase(pk,
"dap-07 input share" || 0x01 || server_role,
input_share_aad, plaintext_input_share)
where pk is the Aggregator's public key; server_role is the Role of
the intended recipient (0x02 for the Leader and 0x03 for the Helper),
plaintext_input_share is the Aggregator's PlaintextInputShare, and
input_share_aad is an encoded message of type InputShareAad defined
below, constructed from the same values as the corresponding fields
in the report. The SealBase() function is as specified in [HPKE],
Section 6.1 for the ciphersuite indicated by the HPKE configuration.
struct {
TaskID task_id;
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
} InputShareAad;
The Leader responds to well-formed requests with HTTP status code 201
Created. Malformed requests are handled as described in Section 3.2.
Clients SHOULD NOT upload the same measurement value in more than one
report if the Leader responds with HTTP status code 201 Created.
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If the Leader does not recognize the task ID, then it MUST abort with
error unrecognizedTask.
The Leader responds to requests whose Leader encrypted input share
uses an out-of-date or unknown HpkeConfig.id value, indicated by
HpkeCiphertext.config_id, with error of type 'outdatedConfig'. When
the Client receives an 'outdatedConfig' error, it SHOULD invalidate
any cached HpkeConfigList and retry with a freshly generated Report.
If this retried upload does not succeed, the Client SHOULD abort and
discontinue retrying.
If a report's ID matches that of a previously uploaded report, the
Leader MUST ignore it. In addition, it MAY alert the Client with
error reportRejected. See the implementation note in
Section 4.5.1.4.
The Leader MUST ignore any report pertaining to a batch that has
already been collected (see Section 4.5.1.4 for details). Otherwise,
comparing the aggregate result to the previous aggregate result may
result in a privacy violation. Note that this is also enforced by
the Helper during the aggregation sub-protocol. The Leader MAY also
abort the upload protocol and alert the Client with error
reportRejected.
The Leader MAY ignore any report whose timestamp is past the task's
task_expiration. When it does so, it SHOULD also abort the upload
protocol and alert the Client with error reportRejected. Client MAY
choose to opt out of the task if its own clock has passed
task_expiration.
The Leader may need to buffer reports while waiting to aggregate them
(e.g., while waiting for an aggregation parameter from the Collector;
see Section 4.6). The Leader SHOULD NOT accept reports whose
timestamps are too far in the future. Implementors MAY provide for
some small leeway, usually no more than a few minutes, to account for
clock skew. If the Leader rejects a report for this reason, it
SHOULD abort the upload protocol and alert the Client with error
reportTooEarly. In this situation, the Client MAY re-upload the
report later on.
If the Leader's input share contains an unrecognized extension, or if
two extensions have the same ExtensionType, then the Leader MAY abort
the upload request with error "invalidMessage". Note that this
behavior is not mandatory because it requires the Leader to decrypt
its input share.
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4.4.3. Upload Extensions
Each PlaintextInputShare carries a list of extensions that Clients
use to convey additional information to the Aggregator. Some
extensions might be intended for both Aggregators; others may only be
intended for a specific Aggregator. (For example, a DAP deployment
might use some out-of-band mechanism for an Aggregator to verify that
reports come from authenticated Clients. It will likely be useful to
bind the extension to the input share via HPKE encryption.)
Each extension is a tag-length encoded value of the following form:
struct {
ExtensionType extension_type;
opaque extension_data<0..2^16-1>;
} Extension;
enum {
TBD(0),
(65535)
} ExtensionType;
Field "extension_type" indicates the type of extension, and
"extension_data" contains information specific to the extension.
Extensions are mandatory-to-implement: If an Aggregator receives a
report containing an extension it does not recognize, then it MUST
reject the report. (See Section 4.5.1.4 for details.)
4.5. Verifying and Aggregating Reports
Once a set of Clients have uploaded their reports to the Leader, the
Leader can begin the process of validating and aggregating them with
the Helper. To enable the system to handle large batches of reports,
this process can be parallelized across many "aggregation jobs" in
which small subsets of the reports are processed independently. Each
aggregation job is associated with exactly one DAP task, but a task
can have many aggregation jobs.
The primary objective of an aggregation job is to run the VDAF
preparation process described in [VDAF], Section 5.2 for each report
in the job. Preparation has two purposes:
1. To "refine" the input shares into "output shares" that have the
desired aggregatable form. For some VDAFs, like Prio3, the
mapping from input to output shares is some fixed, linear
operation, but in general the mapping is controlled dynamically
by the Collector and can be non-linear. In Poplar1, for example,
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the refinement process involves a sequence of "candidate
prefixes" and the output consists of a sequence of zeros and
ones, each indicating whether the corresponding candidate is a
prefix of the measurement from which the input shares were
generated.
2. To verify that the output shares, when combined, correspond to a
valid, refined measurement, where validity is determined by the
VDAF itself. For example, the Prio3Sum variant of Prio3
(Section 7.4.2 of [VDAF]) requires that the output shares sum up
to an integer in a specific range; for Poplar1, the output shares
are required to sum up to a vector that is non-zero in at most
one position.
In general, refinement and verification are not distinct
computations, since for some VDAFs, verification may only be achieved
implicitly as a result of the refinement process. We instead think
of these as properties of the output shares themselves: if
preparation succeeds, then the resulting output shares are guaranteed
to combine into a valid, refined measurement.
VDAF preparation is mapped onto an aggregation job as illustrated in
Figure 2. The protocol is comprised of a sequence of HTTP requests
from the Leader to the Helper, the first of which includes the
aggregation parameter, the Helper's report share for each report in
the job, and for each report the initialization step for preparation.
The Helper's response, along with each subsequent request and
response, carries the remaining messages exchanged during
preparation.
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report, agg_param
|
v
+--------+ +--------+
| Leader | | Helper |
+--------+ +--------+
| AggregationJobInitReq: |
| agg_param, prep_init |
|---------------------------------------------------->|
| AggregationJobResp: |
| prep_resp(continue) |
|<----------------------------------------------------|
| AggregationJobContinueReq: |
| prep_continue |
|---------------------------------------------------->|
| AggregationJobResp: |
| prep_resp(continue) |
|<----------------------------------------------------|
| |
... ...
| |
| AggregationJobContinueReq: |
| prep_continue |
|---------------------------------------------------->|
| AggregationJobResp: |
| prep_resp(continue|finished) |
|<----------------------------------------------------|
| |
v v
leader_out_share helper_out_share
Figure 2: Overview of the DAP aggregation sub-protocol.
The number of steps, and the type of the responses, depends on the
VDAF. The message structures and processing rules are specified in
the following subsections.
In general, reports cannot be aggregated until the Collector
specifies an aggregation parameter. However, in some situations it
is possible to begin aggregation as soon as reports arrive. For
example, Prio3 has just one valid aggregation parameter (the empty
string). And there are use cases for Poplar1 in which aggregation
can begin immediately (i.e., those for which the candidate prefixes/
strings are fixed in advance).
An aggregation job can be thought of as having three phases:
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* Initialization: Begin the aggregation flow by disseminating report
shares and initializing the VDAF prep state for each report.
* Continuation: Continue the aggregation flow by exchanging prep
shares and messages until preparation completes or an error
occurs.
* Completion: Finish the aggregate flow, yielding an output share
corresponding to each report share in the aggregation job.
These phases are described in the following subsections.
4.5.1. Aggregate Initialization
The Leader begins an aggregation job by choosing a set of candidate
reports that pertain to the same DAP task and a job ID which MUST be
unique within the scope of the task. The job ID is a 16-byte value,
structured as follows:
opaque AggregationJobID[16];
The Leader can run this process for many sets of candidate reports in
parallel as needed. After choosing a set of candidates, the Leader
begins aggregation by splitting each report into report shares, one
for each Aggregator. The Leader and Helper then run the aggregate
initialization flow to accomplish two tasks:
1. Recover and determine which input report shares are valid.
2. For each valid report share, initialize the VDAF preparation
process (see Section 5.2 of [VDAF]).
The Leader and Helper initialization behavior is detailed below.
4.5.1.1. Leader Initialization
The Leader begins the aggregate initialization phase with the set of
candidate reports as follows:
1. Generate a fresh AggregationJobID.
2. Decrypt the input share for each report share as described in
Section 4.5.1.3.
3. Check that the resulting input share is valid as described in
Section 4.5.1.4.
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If any step invalidates the report, the Leader rejects the report and
removes it from the set of candidate reports.
Next, for each report the Leader executes the following procedure:
(state, outbound) = Vdaf.ping_pong_leader_init(
vdaf_verify_key,
agg_param,
report_id,
public_share,
plaintext_input_share.payload)
where:
* vdaf_verify_key is the VDAF verification key for the task
* agg_param is the VDAF aggregation parameter provided by the
Collector (see Section 4.6)
* report_id is the report ID, used as the nonce for VDAF sharding
* public_share is the report's public share
* plaintext_input_share is the Leader's PlaintextInputShare
The methods are defined in Section 5.8 of [VDAF]. This process
determines the initial per-report state, as well as the initial
outbound message to send to the Helper. If state is of type
Rejected, then the report is rejected and removed from the set of
candidate reports, and no message is sent to the Helper.
If state is of type Continued, then the Leader constructs a
PrepareInit message structured as follows:
struct {
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext encrypted_input_share;
} ReportShare;
struct {
ReportShare report_share;
opaque payload<0..2^32-1>;
} PrepareInit;
Each of these messages is constructed as follows:
* report_share.report_metadata is the report's metadata.
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* report_share.public_share is the report's public share.
* report_share.encrypted_input_share is the intended recipient's
(i.e. Helper's) encrypted input share.
* payload is set to the outbound message computed by the previous
step.
It is not possible for state to be of type Finished during Leader
initialization.
Once all the report shares have been initialized, the Leader creates
an AggregationJobInitReq message structured as follows:
struct {
QueryType query_type;
select (PartialBatchSelector.query_type) {
case time_interval: Empty;
case fixed_size: BatchID batch_id;
};
} PartialBatchSelector;
struct {
opaque agg_param<0..2^32-1>;
PartialBatchSelector part_batch_selector;
PrepareInit prepare_inits<1..2^32-1>;
} AggregationJobInitReq;
[[OPEN ISSUE: Consider sending report shares separately (in parallel)
to the aggregate instructions. Right now, aggregation parameters and
the corresponding report shares are sent at the same time, but this
may not be strictly necessary.]]
This message consists of:
* agg_param: The VDAF aggregation parameter.
* part_batch_selector: The "partial batch selector" used by the
Aggregators to determine how to aggregate each report:
- For fixed_size tasks, the Leader specifies a "batch ID" that
determines the batch to which each report for this aggregation
job belongs.
[OPEN ISSUE: For fixed_size tasks, the Leader is in complete
control over which batch a report is included in. For
time_interval tasks, the Client has some control, since the
timestamp determines which batch window it falls in. Is this
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desirable from a privacy perspective? If not, it might be
simpler to drop the timestamp altogether and have the agg init
request specify the batch window instead.]
The indicated query type MUST match the task's query type.
Otherwise, the Helper MUST abort with error invalidMessage.
This field is called the "partial" batch selector because,
depending on the query type, it may not determine a batch. In
particular, if the query type is time_interval, the batch is not
determined until the Collector's query is issued (see
Section 4.1).
* prepare_inits: the sequence of PrepareInit messages constructed in
the previous step.
Finally, the Leader sends a PUT request to {helper}/tasks/{task-
id}/aggregation_jobs/{aggregation-job-id}. The payload is set to
AggregationJobInitReq and the media type is set to "application/dap-
aggregation-job-init-req".
The Leader MUST authenticate its requests to the Helper using a
scheme that meets the requirements in Section 3.1.
The Helper's response will be an AggregationJobResp message (see
Section 4.5.1.2. The response's prepare_resps must include exactly
the same report IDs in the same order as the Leader's
AggregationJobInitReq. Otherwise, the Leader MUST abort the
aggregation job.
[[OPEN ISSUE: consider relaxing this ordering constraint. See
issue#217.]]
Otherwise, the Leader proceeds as follows with each report:
1. If the inbound prep response has type "continue", then the Leader
computes
(state, outbound) = Vdaf.ping_pong_leader_continued(agg_param,
prev_state,
inbound)
where:
* agg_param is the VDAF aggregation parameter provided by the
Collector (see Section 4.6)
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* prev_state is the state computed earlier by calling
Vdaf.ping_pong_leader_init or Vdaf.ping_pong_leader_continued
* inbound is the message payload in the PrepareResp
If outbound != None, then the Leader stores state and outbound
and proceeds to Section 4.5.2.1. If outbound == None, then the
preparation process is complete: either state == Rejected(), in
which case the Leader rejects the report and removes it from the
candidate set; or state == Finished(out_share), in which case
preparation is complete and the Leader stores the output share
for use in the collection sub-protocol Section 4.6.
2. Else if the type is "rejected", then the Leader rejects the
report and removes it from the candidate set. The Leader MUST
NOT include the report in a subsequent aggregation job, unless
the error is report_too_early, in which case the Leader MAY
include the report in a subsequent aggregation job.
3. Else the type is invalid, in which case the Leader MUST abort the
aggregation job.
(Note: Since VDAF preparation completes in a constant number of
rounds, it will never be the case that some reports are completed and
others are not.)
4.5.1.2. Helper Initialization
The Helper begins an aggregation job when it receives an
AggregationJobInitReq message from the Leader. For each PrepareInit
conveyed by this message, the Helper attempts to initialize VDAF
preparation (see Section 5.1 of [VDAF]) just as the Leader does. If
successful, it includes the result in its response that the Leader
will use to continue preparing the report.
To begin this process, the Helper checks if it recognizes the task
ID. If not, then it MUST abort with error unrecognizedTask.
Next, the Helper checks that the report IDs in
AggregationJobInitReq.prepare_inits are all distinct. If two
preparation initialization messages have the same report ID, then the
Helper MUST abort with error invalidMessage.
The Helper is now ready to process each report share into an outbound
prepare step to return to the server. The responses will be
structured as follows:
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enum {
continue(0),
finished(1)
reject(2),
(255)
} PrepareRespState;
enum {
batch_collected(0),
report_replayed(1),
report_dropped(2),
hpke_unknown_config_id(3),
hpke_decrypt_error(4),
vdaf_prep_error(5),
batch_saturated(6),
task_expired(7),
invalid_message(8),
report_too_early(9),
(255)
} PrepareError;
struct {
ReportID report_id;
PrepareRespState prepare_resp_state;
select (PrepareResp.prepare_resp_state) {
case continue: opaque payload<0..2^32-1>;
case finished: Empty;
case reject: PrepareError prepare_error;
};
} PrepareResp;
First the Helper preprocesses each report as follows:
1. Decrypt the input share for each report share as described in
Section 4.5.1.3.
2. Check that the resulting input share is valid as described in
Section 4.5.1.4.
For any report that was rejected, the Helper sets the outbound
preparation response to
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error;
} PrepareResp;
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where report_id is the report ID and prepare_error is the indicated
error. For all other reports it initializes the VDAF prep state as
follows (let inbound denote the payload of the prep step sent by the
Leader):
(state, outbound) = Vdaf.ping_pong_helper_init(vdaf_verify_key,
agg_param,
report_id,
public_share,
plaintext_input_share.payload)
where:
* vdaf_verify_key is the VDAF verification key for the task
* agg_param is the VDAF aggregation parameter sent in the
AggregationJobInitReq
* report_id is the report ID
* public_share is the report's public share
* plaintext_input_share is the Helper's PlaintextInputShare
This procedure determines the initial per-report state, as well as
the initial outbound message to send in response to the Leader. If
state is of type Rejected, then the Helper responds with
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error = vdaf_prep_error;
} PrepareResp;
Otherwise the Helper responds with
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = continue;
opaque payload<0..2^32-1> = outbound;
} PrepareResp;
Finally, the Helper creates an AggregationJobResp to send to the
Leader. This message is structured as follows:
struct {
PrepareResp prepare_resps<1..2^32-1>;
} AggregationJobResp;
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where prepare_resps are the outbound prep steps computed in the
previous step. The order MUST match
AggregationJobInitReq.prepare_inits.
The Helper responds to the Leader with HTTP status code 201 Created
and a body consisting of the AggregationJobResp, with media type
"application/dap-aggregation-job-resp".
Changing an aggregation job's parameters is illegal, so further
requests to PUT /tasks/{tasks}/aggregation_jobs/{aggregation-job-id}
for the same aggregation-job-id but with a different
AggregationJobInitReq in the body MUST fail with an HTTP client error
status code.
Additionally, it is not possible to rewind or reset the state of an
aggregation job. Once an aggregation job has been continued at least
once (see Section 4.5.2), further requests to initialize that
aggregation job MUST fail with an HTTP client error status code.
Finally, if state == Continued(prep_state), then the Helper stores
state to prepare for the next continuation step (Section 4.5.2.2).
Otherwise, if state == Finished(out_share), then the Helper stores
out_share for use in the collection sub-protocol (Section 4.6).
4.5.1.3. Input Share Decryption
Each report share has a corresponding task ID, report metadata
(report ID and, timestamp), public share, and the Aggregator's
encrypted input share. Let task_id, report_metadata, public_share,
and encrypted_input_share denote these values, respectively. Given
these values, an Aggregator decrypts the input share as follows.
First, it constructs an InputShareAad message from task_id,
report_metadata, and public_share. Let this be denoted by
input_share_aad. Then, the Aggregator looks up the HPKE config and
corresponding secret key indicated by encrypted_input_share.config_id
and attempts decryption of the payload with the following procedure:
plaintext_input_share = OpenBase(encrypted_input_share.enc, sk,
"dap-07 input share" || 0x01 || server_role,
input_share_aad, encrypted_input_share.payload)
where sk is the HPKE secret key, and server_role is the role of the
Aggregator (0x02 for the Leader and 0x03 for the Helper). The
OpenBase() function is as specified in [HPKE], Section 6.1 for the
ciphersuite indicated by the HPKE configuration. If decryption
fails, the Aggregator marks the report share as invalid with the
error hpke_decrypt_error. Otherwise, the Aggregator outputs the
resulting PlaintextInputShare plaintext_input_share.
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4.5.1.4. Input Share Validation
Validating an input share will either succeed or fail. In the case
of failure, the input share is marked as invalid with a corresponding
PrepareError.
Before beginning the preparation step, Aggregators are required to
perform the following checks:
1. Check that the input share can be decoded as specified by the
VDAF. If not, the input share MUST be marked as invalid with the
error invalid_message.
2. Check if the report is too far into the future. Implementors can
provide for some small leeway, usually no more than a few
minutes, to account for clock skew. If a report is rejected for
this reason, the Aggregator SHOULD mark the input share as
invalid with the error report_too_early.
3. Check if the report's timestamp has passed the task's
task_expiration time. If so, the Aggregator MAY mark the input
share as invalid with the error task_expired.
4. Check if the PlaintextInputShare contains unrecognized
extensions. If so, the Aggregator MUST mark the input share as
invalid with error invalid_message.
5. Check if the ExtensionType of any two extensions in
PlaintextInputShare are the same. If so, the Aggregator MUST
mark the input share as invalid with error invalid_message.
6. Check if the report may still be aggregated with the current
aggregation parameter. This can be done by looking up all
aggregation parameters previously used for this report and
calling
Vdaf.is_valid(current_agg_param, previous_agg_params)
If this returns false, the input share MUST be marked as invalid
with the error report_replayed.
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* Implementation note: To detect replay attacks, each Aggregator
is required to keep track of the set of reports it has
processed for a given task. Because honest Clients choose the
report ID at random, it is sufficient to store the set of IDs
of processed reports. However, implementations may find it
helpful to track additional information, like the timestamp,
so that the storage used for anti-replay can be sharded
efficiently.
7. If the report pertains to a batch that was previously collected,
then make sure the report was already included in all previous
collections for the batch. If not, the input share MUST be
marked as invalid with error batch_collected. This prevents
Collectors from learning anything about small numbers of reports
that are uploaded between two collections of a batch.
* Implementation note: The Leader considers a batch to be
collected once it has completed a collection job for a
CollectionReq message from the Collector; the Helper considers
a batch to be collected once it has responded to an
AggregateShareReq message from the Leader. A batch is
determined by query (Section 4.1) conveyed in these messages.
Queries must satisfy the criteria covered in Section 4.6.5.
These criteria are meant to restrict queries in a way make it
easy to determine wither a report pertains to a batch that was
collected.
[TODO: If a section to clarify report and batch states is
added this can be removed. See Issue #384]
8. Depending on the query type for the task, additional checks may
be applicable:
* For fixed_size tasks, the Aggregators need to ensure that each
batch is roughly the same size. If the number of reports
aggregated for the current batch exceeds the maximum batch
size (per the task configuration), the Aggregator MAY mark the
input share as invalid with the error batch_saturated. Note
that this behavior is not strictly enforced here but during
the collect sub-protocol. (See Section 4.6.5.) If both
checks succeed, the input share is not marked as invalid.
9. Finally, if an Aggregator cannot determine if an input share is
valid, it MUST mark the input share as invalid with error
report_dropped. For example, if the Aggregator has evicted the
state required to perform the check from long-term storage. (See
Section 5.4.1 for details.)
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If all of the above checks succeed, the input share is not marked as
invalid.
4.5.2. Aggregate Continuation
In the continuation phase, the Leader drives the VDAF preparation of
each report in the candidate report set until the underlying VDAF
moves into a terminal state, yielding an output share for both
Aggregators or a rejection.
Whether this phase is reached depends on the VDAF: for 1-round VDAFs,
like Prio3, processing has already completed. Continuation is
required for VDAFs that require more than one round.
4.5.2.1. Leader Continuation
The Leader begins each step of aggregation continuation with a prep
state object state and an outbound message outbound for each report
in the candidate set.
The Leader advances its aggregation job to the next step (step 1 if
this is the first continuation after initialization). Then it
instructs the Helper to advance the aggregation job to the step the
Leader has just reached. For each report the Leader constructs a
preparation continuation message:
struct {
ReportID report_id;
opaque payload<0..2^32-1>;
} PrepareContinue;
where report_id is the report ID associated with state and outbound,
and payload is set to the outbound message.
Next, the Leader sends a POST request to the aggregation job URI used
during initialization (see Section 4.5.1.1) with media type
"application/dap-aggregation-job-continue-req" and body structured
as:
struct {
uint16 step;
PrepareContinue prepare_continues<1..2^32-1>;
} AggregationJobContinueReq;
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The step field is the step of DAP aggregation that the Leader just
reached and wants the Helper to advance to. The prepare_continues
field is the sequence of preparation continuation messages
constructed in the previous step. The PrepareContinues MUST be in
the same order as the previous aggregate request.
The Leader MUST authenticate its requests to the Helper using a
scheme that meets the requirements in Section 3.1.
The Helper's response will be an AggregationJobResp message (see
Section 4.5.1.2). The response's prepare_resps must include exactly
the same report IDs in the same order as the Leader's
AggregationJobContinueReq. Otherwise, the Leader MUST abort the
aggregation job.
[[OPEN ISSUE: consider relaxing this ordering constraint. See
issue#217.]]
Otherwise, the Leader proceeds as follows with each report:
1. If the inbound prep response type is "continue" and the state is
Continued(prep_state), then the Leader computes
(state, outbound) = Vdaf.ping_pong_leader_continued(agg_param,
state,
inbound)
where inbound is the message payload. If outbound != None, then
the Leader stores state and outbound and proceeds to another
continuation step. If outbound == None, then the preparation
process is complete: either state == Rejected(), in which case
the Leader rejects the report and removes it from the candidate
set; or state == Finished(out_share), in which case preparation
is complete and the Leader stores the output share for use in the
collection sub-protocol Section 4.6.
2. Else if the type is "finished" and state == Finished(out_share),
then preparation is complete and the Leader stores the output
share for use in the collection flow (Section 4.6).
3. Else if the type is "reject", then the Leader rejects the report
and removes it from the candidate set.
4. Else the type is invalid, in which case the Leader MUST abort the
aggregation job.
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4.5.2.2. Helper Continuation
The Helper begins each step of continuation with a sequence of state
objects, which will be Continued(prep_state), one for each report in
the candidate set.
The Helper awaits an HTTP POST request to the aggregation job URI
from the Leader, the body of which is an AggregationJobContinueReq as
specified in Section 4.5.2.1.
Next, it checks that it recognizes the task ID. If not, then it MUST
abort with error unrecognizedTask.
Next, it checks if it recognizes the indicated aggregation job ID.
If not, it MUST abort with error unrecognizedAggregationJob.
Next, the Helper checks that:
1. the report IDs are all distinct
2. each report ID corresponds to one of the state objects
3. AggregationJobContinueReq.step is not equal to 0
If any of these checks fail, then the Helper MUST abort with error
invalidMessage. Additionally, if any prep step appears out of order
relative to the previous request, then the Helper MAY abort with
error invalidMessage. (Note that a report may be missing, in which
case the Helper should assume the Leader rejected it.)
[OPEN ISSUE: Issue 438: It may be useful for the Leader to explicitly
signal rejection.]
Next, the Helper checks if the continuation step indicated by the
request is correct. (For the first AggregationJobContinueReq the
value should be 1; for the second the value should be 2; and so on.)
If the Leader is one step behind (e.g., the Leader has resent the
previous HTTP request), then the Helper MAY attempt to recover by re-
sending the previous AggregationJobResp. In this case it SHOULD
verify that the contents of the AggregationJobContinueReq are
identical to the previous message (see Section 4.5.2.3). Otherwise,
if the step is incorrect, the Helper MUST abort with error
stepMismatch.
The Helper is now ready to continue preparation for each report. Let
inbound denote the payload of the prep step. The Helper computes the
following:
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(state, outbound) = Vdaf.ping_pong_helper_continued(agg_param,
state,
inbound)
If state == Rejected(), then the Helper's response is
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error = vdaf_prep_error;
} PrepareResp;
Otherwise, if outbound != None, then the Helper's response is
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = continue;
opaque payload<0..2^32-1> = outbound;
} PrepareResp;
Otherwise, if outbound == None, then the Helper's resposne is
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = finished;
} PrepareResp;
Next, the Helper constructs an AggregationJobResp message
(Section 4.5.1.2) with each prep step. The order of the prep steps
MUST match the Leader's request. It responds to the Leader with HTTP
status 200 OK, media type application/dap-aggregation-job-resp, and a
body consisting of the AggregationJobResp.
Finally, if state == Continued(prep_state), then the Helper stores
state to prepare for the next continuation step (Section 4.5.2.2).
Otherwise, if state == Finished(out_share), then the Helper stores
out_share for use in the collection sub-protocol (Section 4.6).
4.5.2.3. Recovering from Aggregation Step Skew
AggregationJobContinueReq messages contain a step field, allowing
Aggregators to ensure that their peer is on an expected step of the
DAP aggregation protocol. In particular, the intent is to allow
recovery from a scenario where the Helper successfully advances from
step n to n+1, but its AggregationJobResp response to the Leader gets
dropped due to something like a transient network failure. The
Leader could then resend the request to have the Helper advance to
step n+1 and the Helper should be able to retransmit the
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AggregationJobContinueReq that was previously dropped. To make that
kind of recovery possible, Aggregator implementations SHOULD
checkpoint the most recent step's prep state and messages to durable
storage such that the Leader can re-construct continuation requests
and the Helper can re-construct continuation responses as needed.
When implementing an aggregation step skew recovery strategy, the
Helper SHOULD ensure that the Leader's AggregationJobContinueReq
message did not change when it was re-sent (i.e., the two messages
must be identical). This prevents the Leader from re-winding an
aggregation job and re-running an aggregation step with different
parameters.
[[OPEN ISSUE: Allowing the Leader to "rewind" aggregation job state
of the Helper may allow an attack on privacy. For instance, if the
VDAF verification key changes, the prep shares in the Helper's
response would change even if the consistency check is made.
Security analysis is required. See #401.]]
One way the Helper could address this would be to store a digest of
the Leader's request, indexed by aggregation job ID and step, and
refuse to service a request for a given aggregation step unless it
matches the previously seen request (if any).
4.6. Collecting Results
In this phase, the Collector requests aggregate shares from each
Aggregator and then locally combines them to yield a single aggregate
result. In particular, the Collector issues a query to the Leader
(Section 4.1), which the Aggregators use to select a batch of reports
to aggregate. Each Aggregator emits an aggregate share encrypted to
the Collector so that it can decrypt and combine them to yield the
aggregate result. This entire process is composed of two
interactions:
1. Collect request and response between the Collector and Leader,
specified in Section 4.6.1
2. Aggregate share request and response between the Leader and the
Helper, specified in Section 4.6.2
Once complete, the Collector computes the final aggregate result as
specified in Section 4.6.3.
This overall process is referred to as a "collection job".
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4.6.1. Collection Job Initialization
First, the Collector chooses a collection job ID:
opaque CollectionJobID[16];
This ID value MUST be unique within the scope of the corresponding
DAP task.
To initiate the collection job, the collector issues a PUT request to
{leader}/tasks/{task-id}/collection_jobs/{collection-job-id}. The
body of the request has media type "application/dap-collect-req", and
it is structured as follows:
struct {
Query query;
opaque agg_param<0..2^32-1>; /* VDAF aggregation parameter */
} CollectionReq;
The named parameters are:
* query, the Collector's query. The indicated query type MUST match
the task's query type. Otherwise, the Leader MUST abort with
error "invalidMessage".
* agg_param, an aggregation parameter for the VDAF being executed.
This is the same value as in AggregationJobInitReq (see
Section 4.5.1.1).
Collectors MUST authenticate their requests to Leaders using a scheme
that meets the requirements in Section 3.1.
Depending on the VDAF scheme and how the Leader is configured, the
Leader and Helper may already have prepared a sufficient number of
reports satisfying the query and be ready to return the aggregate
shares right away. However, this is not always the case. In fact,
for some VDAFs, it is not be possible to begin running aggregation
jobs (Section 4.5) until the Collector initiates a collection job.
This is because, in general, the aggregation parameter is not known
until this point. In certain situations it is possible to predict
the aggregation parameter in advance. For example, for Prio3 the
only valid aggregation parameter is the empty string. For these
reasons, the collection job is handled asynchronously.
Upon receipt of a CollectionReq, the Leader begins by checking that
it recognizes the task ID in the request path. If not, it MUST abort
with error unrecognizedTask.
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The Leader MAY further validate the request according to the
requirements in Section 4.6.5 and abort with the indicated error,
though some conditions such as the number of valid reports may not be
verifiable while handling the CollectionReq message, and the batch
will have to be re-validated later on regardless.
If the Leader finds the CollectionReq to be valid, it immediately
responds with HTTP status 201.
The Leader then begins working with the Helper to aggregate the
reports satisfying the query (or continues this process, depending on
the VDAF) as described in Section 4.5.
Changing a collection job's parameters is illegal, so further
requests to PUT /tasks/{tasks}/collection_jobs/{collection-job-id}
for the same collection-job-id but with a different CollectionReq in
the body MUST fail with an HTTP client error status code.
After receiving the response to its CollectionReq, the Collector
makes an HTTP POST request to the collection job URI to check on the
status of the collect job and eventually obtain the result. If the
collection job is not finished yet, the Leader responds with HTTP
status 202 Accepted. The response MAY include a Retry-After header
field to suggest a polling interval to the Collector.
Asynchronously from any request from the Collector, the Leader
attempts to run the collection job. It first checks whether it can
construct a batch for the collection job by applying the requirements
in Section 4.6.5. If so, then the Leader obtains the Helper's
aggregate share following the aggregate-share request flow described
in Section 4.6.2. If not, it either aborts the collection job or
tries again later, depending on which requirement in Section 4.6.5
was not met. If the Leader has a pending aggregation job that
overlaps with the batch for the collection job, the Leader MUST first
complete the aggregation job before proceeding and requesting an
aggregate share from the Helper. After the collection job has been
created, the Leader MUST NOT schedule new aggregation jobs that
overlap with the batch for the collection job. This avoids a race
condition between aggregation and collection jobs that can yield
trivial batch mismatch errors.
Once both aggregate shares are successfully obtained, the Leader
responds to subsequent HTTP POST requests to the collection job with
HTTP status code 200 OK and a body consisting of a Collection:
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struct {
PartialBatchSelector part_batch_selector;
uint64 report_count;
Interval interval;
HpkeCiphertext leader_encrypted_agg_share;
HpkeCiphertext helper_encrypted_agg_share;
} Collection;
The body's media type is "application/dap-collection". The
Collection structure includes the following:
* part_batch_selector: Information used to bind the aggregate result
to the query. For fixed_size tasks, this includes the batch ID
assigned to the batch by the Leader. The indicated query type
MUST match the task's query type.
[OPEN ISSUE: What should the Collector do if the query type
doesn't match?]
* report_count: The number of reports included in the batch.
* interval: The smallest interval of time that contains the
timestamps of all reports included in the batch, such that the
interval's start and duration are both multiples of the task's
time_precision parameter. Note that in the case of a
time_interval type query (see Section 4.1), this interval can be
smaller than the one in the corresponding CollectionReq.query.
* leader_encrypted_agg_share: The Leader's aggregate share,
encrypted to the Collector.
* helper_encrypted_agg_share: The Helper's aggregate share,
encrypted to the Collector.
If obtaining aggregate shares fails, then the Leader responds to
subsequent HTTP POST requests to the collection job with an HTTP
error status and a problem document as described in Section 3.2.
The Leader MAY respond with HTTP status 204 No Content to requests to
a collection job if the results have been deleted.
The Collector can send an HTTP DELETE request to the collection job,
which indicates to the Leader that it can abandon the collection job
and discard all state related to it.
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4.6.1.1. A Note on Idempotence
The reason a POST is used to poll the state of a collection job
instead of a GET is because of the fixed-size query mode (see
Section 4.1.2). Collectors may make a query against the current
batch, and it is the Leader's responsibility to keep track of what
batch is current for some task. Polling a collection job is the only
point at which it is safe for the Leader to change its set of current
batches, since it constitutes acknowledgement on the Collector's part
that it received the response to some previous PUT request to the
collection jobs resource.
This means that polling a collection job can have the side effect of
changing the set of current batches in the Leader, and thus using a
GET is inappropriate.
4.6.2. Obtaining Aggregate Shares
The Leader must obtain the Helper's encrypted aggregate share before
it can complete a collection job. To do this, the Leader first
computes a checksum over the reports included in the batch. The
checksum is computed by taking the SHA256 [SHS] hash of each report
ID from the Client reports included in the aggregation, then
combining the hash values with a bitwise-XOR operation.
Then the Leader sends a POST request to {helper}/tasks/{task-
id}/aggregate_shares with the following message:
struct {
QueryType query_type;
select (BatchSelector.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: BatchID batch_id;
};
} BatchSelector;
struct {
BatchSelector batch_selector;
opaque agg_param<0..2^32-1>;
uint64 report_count;
opaque checksum[32];
} AggregateShareReq;
The media type of the request is "application/dap-aggregate-share-
req". The message contains the following parameters:
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* batch_selector: The "batch selector", which encodes parameters
used to determine the batch being aggregated. The value depends
on the query type for the task:
- For time_interval tasks, the request specifies the batch
interval.
- For fixed_size tasks, the request specifies the batch ID.
The indicated query type MUST match the task's query type.
Otherwise, the Helper MUST abort with "invalidMessage".
* agg_param: The opaque aggregation parameter for the VDAF being
executed. This value MUST match the AggregationJobInitReq message
for each aggregation job used to compute the aggregate shares (see
Section 4.5.1.1) and the aggregation parameter indicated by the
Collector in the CollectionReq message (see Section 4.6.1).
* report_count: The number number of reports included in the batch.
* checksum: The batch checksum.
Leaders MUST authenticate their requests to Helpers using a scheme
that meets the requirements in Section 3.1.
To handle the Leader's request, the Helper first ensures that it
recognizes the task ID in the request path. If not, it MUST abort
with error unrecognizedTask. The Helper then verifies that the
request meets the requirements for batch parameters following the
procedure in Section 4.6.5.
Next, it computes a checksum based on the reports that satisfy the
query, and checks that the report_count and checksum included in the
request match its computed values. If not, then it MUST abort with
an error of type "batchMismatch".
Next, it computes the aggregate share agg_share corresponding to the
set of output shares, denoted out_shares, for the batch interval, as
follows:
agg_share = Vdaf.aggregate(agg_param, out_shares)
Implementation note: For most VDAFs, it is possible to aggregate
output shares as they arrive rather than wait until the batch is
collected. To do so however, it is necessary to enforce the batch
parameters as described in Section 4.6.5 so that the Aggregator knows
which aggregate share to update.
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The Helper then encrypts agg_share under the Collector's HPKE public
key as described in Section 4.6.4, yielding encrypted_agg_share.
Encryption prevents the Leader from learning the actual result, as it
only has its own aggregate share and cannot compute the Helper's.
The Helper responds to the Leader with HTTP status code 200 OK and a
body consisting of an AggregateShare, with media type "application/
dap-aggregate-share":
struct {
HpkeCiphertext encrypted_aggregate_share;
} AggregateShare;
encrypted_aggregate_share.config_id is set to the Collector's HPKE
config ID. encrypted_aggregate_share.enc is set to the encapsulated
HPKE context enc computed above and
encrypted_aggregate_share.ciphertext is the ciphertext
encrypted_agg_share computed above.
The Helper's handling of this request MUST be idempotent. That is,
if multiple identical, valid AggregateShareReqs are received, they
should all yield the same response while only consuming one unit of
the task's max_batch_query_count (see Section 4.6.5).
After receiving the Helper's response, the Leader uses the
HpkeCiphertext to finalize a collection job (see Section 4.6.3).
Once an AggregateShareReq has been issued for the batch determined by
a given query, it is an error for the Leader to issue any more
aggregation jobs for additional reports that satisfy the query.
These reports will be rejected by the Helper as described in
Section 4.5.1.4.
Before completing the collection job, the Leader also computes its
own aggregate share agg_share by aggregating all of the prepared
output shares that fall within the batch interval. Finally, it
encrypts its aggregate share under the Collector's HPKE public key as
described in Section 4.6.4.
4.6.3. Collection Job Finalization
Once the Collector has received a collection job from the Leader, it
can decrypt the aggregate shares and produce an aggregate result.
The Collector decrypts each aggregate share as described in
Section 4.6.4. Once the Collector successfully decrypts all
aggregate shares, it unshards the aggregate shares into an aggregate
result using the VDAF's unshard algorithm. In particular, let
leader_agg_share denote the Leader's aggregate share,
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helper_agg_share denote the Helper's aggregate share, let
report_count denote the report count sent by the Leader, and let
agg_param be the opaque aggregation parameter. The final aggregate
result is computed as follows:
agg_result = Vdaf.unshard(agg_param,
[leader_agg_share, helper_agg_share],
report_count)
4.6.4. Aggregate Share Encryption
Encrypting an aggregate share agg_share for a given AggregateShareReq
is done as follows:
enc, payload = SealBase(pk, "dap-07 aggregate share" || server_role || 0x00,
agg_share_aad, agg_share)
where pk is the HPKE public key encoded by the Collector's HPKE key,
server_role is the role of the encrypting server (0x02 for the Leader
and 0x03 for a Helper), and agg_share_aad is a value of type
AggregateShareAad. The SealBase() function is as specified in
[HPKE], Section 6.1 for the ciphersuite indicated by the HPKE
configuration.
struct {
TaskID task_id;
opaque agg_param<0..2^32-1>;
BatchSelector batch_selector;
} AggregateShareAad;
* task_id is the ID of the task the aggregate share was computed in.
* agg_param is the aggregation parameter used to compute the
aggregate share.
* batch_selector is the is the batch selector from the
AggregateShareReq (for the Helper) or the batch selector computed
from the Collector's query (for the Leader).
The Collector decrypts these aggregate shares using the opposite
process. Specifically, given an encrypted input share, denoted
enc_share, for a given batch selector, decryption works as follows:
agg_share = OpenBase(enc_share.enc, sk, "dap-07 aggregate share" ||
server_role || 0x00, agg_share_aad, enc_share.payload)
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where sk is the HPKE secret key, server_role is the role of the
server that sent the aggregate share (0x02 for the Leader and 0x03
for the Helper), and agg_share_aad is an AggregateShareAad message
constructed from the task ID and the aggregation parameter in the
collect request, and a batch selector. The value of the batch
selector used in agg_share_aad is computed by the Collector from its
query and the response to its query as follows:
* For time_interval tasks, the batch selector is the batch interval
specified in the query.
* For fixed_size tasks, the batch selector is the batch ID assigned
sent in the response.
The OpenBase() function is as specified in [HPKE], Section 6.1 for
the ciphersuite indicated by the HPKE configuration.
4.6.5. Batch Validation
Before a Leader runs a collection job or a Helper responds to an
AggregateShareReq, it must first check that the job or request does
not violate the parameters associated with the DAP task. It does so
as described here. Where we say that an Aggregator MUST abort with
some error, then:
* Leaders should respond to subsequent HTTP POST requests to the
collection job with the indicated error.
* Helpers should respond to the AggregateShareReq with the indicated
error.
First the Aggregator checks that the batch respects any "boundaries"
determined by the query type. These are described in the subsections
below. If the boundary check fails, then the Aggregator MUST abort
with an error of type "batchInvalid".
Next, the Aggregator checks that batch contains a valid number of
reports, as determined by the query type. If the size check fails,
then Helpers MUST abort with an error of type "invalidBatchSize".
Leaders SHOULD wait for more reports to be validated and try the
collection job again later.
Next, the Aggregator checks that the batch has not been queried too
many times. This is determined by the maximum number of times a
batch can be queried, max_batch_query_count. If the batch has been
queried with more than max_batch_query_count distinct aggregation
parameters, the Aggregator MUST abort with error of type
"batchQueriedTooManyTimes".
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Finally, the Aggregator checks that the batch does not contain a
report that was included in any previous batch. If this batch
overlap check fails, then the Aggregator MUST abort with error of
type "batchOverlap". For time_interval tasks, it is sufficient (but
not necessary) to check that the batch interval does not overlap with
the batch interval of any previous query. If this batch interval
check fails, then the Aggregator MAY abort with error of type
"batchOverlap".
[[OPEN ISSUE: #195 tracks how we might relax this constraint to allow
for more collect query flexibility. As of now, this is quite rigid
and doesn't give the Collector much room for mistakes.]]
4.6.5.1. Time-interval Queries
4.6.5.1.1. Boundary Check
The batch boundaries are determined by the time_precision field of
the query configuration. For the batch_interval included with the
query, the Aggregator checks that:
* batch_interval.duration >= time_precision (this field determines,
effectively, the minimum batch duration)
* both batch_interval.start and batch_interval.duration are
divisible by time_precision
These measures ensure that Aggregators can efficiently "pre-
aggregate" output shares recovered during the aggregation sub-
protocol.
4.6.5.1.2. Size Check
The query configuration specifies the minimum batch size,
min_batch_size. The Aggregator checks that len(X) >= min_batch_size,
where X is the set of reports successfully aggregated into the batch.
4.6.5.2. Fixed-size Queries
4.6.5.2.1. Boundary Check
For fixed_size tasks, the batch boundaries are defined by opaque
batch IDs. Thus the Aggregator needs to check that the query is
associated with a known batch ID:
* For a CollectionReq containing a query of type by_batch_id, the
Leader checks that the provided batch ID corresponds to a batch ID
it returned in a previous collection for the task.
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* For an AggregateShareReq, the Helper checks that the batch ID
provided by the Leader corresponds to a batch ID used in a
previous AggregationJobInitReq for the task.
4.6.5.2.2. Size Check
The query configuration specifies the minimum batch size,
min_batch_size, and maximum batch size, max_batch_size. The
Aggregator checks that len(X) >= min_batch_size and len(X) <=
max_batch_size, where X is the set of reports successfully aggregated
into the batch.
5. Operational Considerations
The DAP protocol has inherent constraints derived from the tradeoff
between privacy guarantees and computational complexity. These
tradeoffs influence how applications may choose to utilize services
implementing the specification.
5.1. Protocol participant capabilities
The design in this document has different assumptions and
requirements for different protocol participants, including Clients,
Aggregators, and Collectors. This section describes these
capabilities in more detail.
5.1.1. Client capabilities
Clients have limited capabilities and requirements. Their only
inputs to the protocol are (1) the parameters configured out of band
and (2) a measurement. Clients are not expected to store any state
across any upload flows, nor are they required to implement any sort
of report upload retry mechanism. By design, the protocol in this
document is robust against individual Client upload failures since
the protocol output is an aggregate over all inputs.
5.1.2. Aggregator capabilities
Leaders and Helpers have different operational requirements. The
design in this document assumes an operationally competent Leader,
i.e., one that has no storage or computation limitations or
constraints, but only a modestly provisioned Helper, i.e., one that
has computation, bandwidth, and storage constraints. By design,
Leaders must be at least as capable as Helpers, where Helpers are
generally required to:
* Support the aggregate sub-protocol, which includes validating and
aggregating reports; and
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* Publish and manage an HPKE configuration that can be used for the
upload protocol.
In addition, for each DAP task, the Helper is required to:
* Implement some form of batch-to-report index, as well as inter-
and intra-batch replay mitigation storage, which includes some way
of tracking batch report size. Some of this state may be used for
replay attack mitigation. The replay mitigation strategy is
described in Section 4.5.1.4.
Beyond the minimal capabilities required of Helpers, Leaders are
generally required to:
* Support the upload protocol and store reports; and
* Track batch report size during each collect flow and request
encrypted output shares from Helpers.
In addition, for each DAP task, the Leader is required to:
* Implement and store state for the form of inter- and intra-batch
replay mitigation in Section 4.5.1.4.
5.1.3. Collector capabilities
Collectors statefully interact with Aggregators to produce an
aggregate output. Their input to the protocol is the task
parameters, configured out of band, which include the corresponding
batch window and size. For each collect invocation, Collectors are
required to keep state from the start of the protocol to the end as
needed to produce the final aggregate output.
Collectors must also maintain state for the lifetime of each task,
which includes key material associated with the HPKE key
configuration.
5.2. Data resolution limitations
Privacy comes at the cost of computational complexity. While affine-
aggregatable encodings (AFEs) can compute many useful statistics,
they require more bandwidth and CPU cycles to account for finite-
field arithmetic during input-validation. The increased work from
verifying inputs decreases the throughput of the system or the inputs
processed per unit time. Throughput is related to the verification
circuit's complexity and the available compute-time to each
Aggregator.
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Applications that utilize proofs with a large number of
multiplication gates or a high frequency of inputs may need to limit
inputs into the system to meet bandwidth or compute constraints.
Some methods of overcoming these limitations include choosing a
better representation for the data or introducing sampling into the
data collection methodology.
[[TODO: Discuss explicit key performance indicators, here or
elsewhere.]]
5.3. Aggregation utility and soft batch deadlines
A soft real-time system should produce a response within a deadline
to be useful. This constraint may be relevant when the value of an
aggregate decreases over time. A missed deadline can reduce an
aggregate's utility but not necessarily cause failure in the system.
An example of a soft real-time constraint is the expectation that
input data can be verified and aggregated in a period equal to data
collection, given some computational budget. Meeting these deadlines
will require efficient implementations of the input-validation
protocol. Applications might batch requests or utilize more
efficient serialization to improve throughput.
Some applications may be constrained by the time that it takes to
reach a privacy threshold defined by a minimum number of reports.
One possible solution is to increase the reporting period so more
samples can be collected, balanced against the urgency of responding
to a soft deadline.
5.4. Protocol-specific optimizations
Not all DAP tasks have the same operational requirements, so the
protocol is designed to allow implementations to reduce operational
costs in certain cases.
5.4.1. Reducing storage requirements
In general, the Aggregators are required to keep state for tasks and
all valid reports for as long as collect requests can be made for
them. In particular, Aggregators must store a batch as long as the
batch has not been queried more than max_batch_query_count times.
However, it is not always necessary to store the reports themselves.
For schemes like Prio3 [VDAF] in which reports are verified only
once, each Aggregator only needs to store its aggregate share for
each possible batch interval, along with the number of times the
aggregate share was used in a batch. This is due to the requirement
that the batch interval respect the boundaries defined by the DAP
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parameters. (See Section 4.6.5.)
However, Aggregators are also required to implement several per-
report checks that require retaining a number of data artifacts. For
example, to detect replay attacks, it is necessary for each
Aggregator to retain the set of report IDs of reports that have been
aggregated for the task so far. Depending on the task lifetime and
report upload rate, this can result in high storage costs. To
alleviate this burden, DAP allows Aggregators to drop this state as
needed, so long as reports are dropped properly as described in
Section 4.5.1.4. Aggregators SHOULD take steps to mitigate the risk
of dropping reports (e.g., by evicting the oldest data first).
Furthermore, the Aggregators must store data related to a task as
long as the current time has not passed this task's task_expiration.
Aggregator MAY delete the task and all data pertaining to this task
after task_expiration. Implementors SHOULD provide for some leeway
so the Collector can collect the batch after some delay.
6. Compliance Requirements
In the absence of an application or deployment-specific profile
specifying otherwise, a compliant DAP application MUST implement the
following HPKE cipher suite:
* KEM: DHKEM(X25519, HKDF-SHA256) (see [HPKE], Section 7.1)
* KDF: HKDF-SHA256 (see [HPKE], Section 7.2)
* AEAD: AES-128-GCM (see [HPKE], Section 7.3)
7. Security Considerations
DAP assumes an active attacker that controls the network and has the
ability to statically corrupt any number of Clients, Aggregators, and
Collectors. That is, the attacker can learn the secret state of any
party prior to the start of its attack. For example, it may coerce a
Client into providing malicious input shares for aggregation or
coerce an Aggregator into diverting from the protocol specified
(e.g., by divulging its input shares to the attacker).
In the presence of this adversary, DAP aims to achieve the privacy
and robustness security goals described in [VDAF]'s Security
Considerations section. Even if DAP achieves those goals, there are
still some threats it does not defend against:
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1. Even benign collect requests may leak information beyond what one
might expect intuitively. For example, the Poplar1 VDAF [VDAF]
can be used to compute the set of heavy hitters among a set of
arbitrary bit strings uploaded by Clients. This requires
multiple evaluations of the VDAF, the results of which reveal
information to the Aggregators and Collector beyond what follows
from the heavy hitters themselves. Or the result of the Prio3Sum
VDAF could leak information about outlier values. Note that this
leakage can be mitigated using differential privacy
(Section 7.5).
2. On its own, DAP does not defend against Sybil attacks. See
Section 7.2 for discussion and potential mitigations.
7.1. Threat model
In this section, we enumerate the actors participating in a
Distributed Aggregation Protocol deployment, enumerate their assets
(secrets that are either inherently valuable or which confer some
capability that enables further attack on the system), the
capabilities that a malicious or compromised actor has, and potential
mitigations for attacks enabled by those capabilities.
This model assumes that all participants have previously agreed upon
and exchanged all shared parameters over some unspecified secure
channel.
7.1.1. Client/user
7.1.1.1. Assets
1. Unsharded measurements. Clients are the only actor that can ever
see the original measurements.
2. Unencrypted input shares.
7.1.1.2. Capabilities and mitigations
1. Individual users can reveal their own measurement and compromise
their own privacy.
2. Clients may affect the quality of aggregate results by reporting
false measurements.
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* Prio can only prove that a submitted measurement is valid, not
that it is true. False measurements can be mitigated
orthogonally to the Prio protocol (e.g., by requiring that
batches include a minimum number of contributions) and so
these attacks are considered to be outside of the threat
model.
3. Clients may upload reports to a task multiple times. The VDAF
will prove that each report is valid, but the results of a VDAF
like Prio3Sum can be skewed if a Client submits many valid
reports. Attackers may also attempt ballot stuffing attacks,
trying to produce aggregations over batches containing nothing
but synthetic reports with a known value and a single, legitimate
report whose privacy is then compromised.
* This attack can be mitigated if DAP deployments require
Clients to authenticate when uploading (see Section 7.2.1),
which would allow enforcing policy like a maximum number of
uploads per day.
* Applying differential privacy to either measurements before
sharding them into reports or to aggregate shares
(Section 7.5) can protect isolated legitimate measurements.
7.1.2. Aggregator
7.1.2.1. Assets
1. Unencrypted input shares.
2. Input share decryption keys.
3. Client identifying information.
4. Aggregate shares.
5. Aggregator identity.
7.1.2.2. Capabilities and mitigations
1. Aggregators may defeat the robustness of the system by emitting
incorrect aggregate shares.
* There is no way for aggregators to detect a fraudulent
aggregate shares except by applying heuristics to aggregate
results that are outside of DAP's scope. For instance it may
be apparent from the aggregate result that one or more
aggregators have emitted an incorrect aggregate share.
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2. If Clients reveal identifying information to Aggregators (such as
a trusted identity during Client authentication), Aggregators can
learn which Clients are contributing reports.
1. Aggregators may reveal that a particular Client contributed
reports.
2. Aggregators may attack robustness by selectively omitting
reports from certain Clients.
* For example, omitting submissions from a particular
geographic region to falsely suggest that a particular
localization is not being used. * Exposing metadata to
Aggregators can be mitigated by deploying an anonymizing
proxy (see Section 7.3).
3. Individual Aggregators may compromise availability of the system
by refusing to emit aggregate shares.
* The DAP and VDAF threat model already assumes that robustness
only holds if both aggregators are honest, so a loss of
availability is no worse.
4. Violate robustness. Any Aggregator can collude with a malicious
Client to craft a proof that will fool honest Aggregators into
accepting invalid measurements.
* The VDAF threat model already assumes that robustness only
holds if both aggregators are honest.
5. Aggregators (and the Collector) can count the total number of
input shares, which could compromise user privacy (and
differential privacy Section 7.5) if the presence or absence of a
share for a given user is sensitive.
* Clients can ensure that aggregate counts are non-sensitive by
generating reports independently of user behavior (see
Section 7.1.5.
* Clients, especially in deployments that cannot schedule report
uploads at a fixed time (e.g., an application that does not
run persistently) can also apply local differential privacy to
measurements before constructing reports.
[[TODO: link to the Shan et al. I-D on differential privacy in DAP
once it is published.]]
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7.1.3. Leader
The Leader is also an Aggregator, and so all the assets, capabilities
and mitigations available to Aggregators also apply to the Leader.
7.1.3.1. Capabilities and mitigations
1. Shrinking the anonymity set. The Leader instructs the Helper to
construct aggregate shares and so could request aggregations over
dangerously few reports.
1. This capability is particularly strong in the case of fixed-
size queries (Section 4.1.2), because in that setting, the
Leader is responsible for assigning reports to batches and so
can craft batches to target certain contributions. * This is
mitigated by choosing a sufficient minimum batch size for the
task. * If aggregate shares emitted by Aggregators satisfy
differential privacy Section 7.5, then genuine records are
protected regardless of the size of the anonymity set.
2. Relaying messages between Helper and Collector in the collect
sub-protocol. These messages are not authenticated, meaning the
leader can:
1. Send collect parameters to the Helper that do not reflect the
parameters chosen by the Collector
* This is mitigated by including the BatchSelector and
aggregation parameter in the AAD used to encrypt aggregate
shares.
2. Discard the aggregate share computed by the Helper and then
fabricate aggregate shares that combine into an arbitrary
aggregate result * These are attacks on robustness, which we
already assume to hold only if both Aggregators are honest,
putting these malicious-Leader attacks out of scope.
[[OPEN ISSUE: Should we have authentication in either direction
between the Helper and the Collector? #155]]
7.1.4. Aggregator collusion
If all Aggregators collude (e.g. by promiscuously sharing unencrypted
input shares), then none of the properties of the system hold.
Accordingly, such scenarios are outside of the threat model.
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7.1.5. Attacker on the network
We assume the existence of attackers on the network links between
participants. Most passive network attacks are mitigated by DAP's
requirement of HTTPS for all traffic and mutual authentication for
key protocol interactions (see Section 3). Nonetheless, there remain
information leaks that deployments should be aware of.
7.1.5.1. Capabilities
1. Attackers may observe messages exchanged between participants at
the IP layer.
1. The attacker can observe source and destination IP addresses,
potentially revealing the existence of Clients and
Aggregators.
2. The time of upload of reports by Clients could reveal
information about user activity. For example, if a user opts
into a new feature, and the Client immediately reports this
to Aggregators, then just by observing network traffic, the
attacker can infer what the user did.
3. Observation of message size could allow the attacker to learn
how many reports are being uploaded by a Client. For
example, if the attacker observes an encrypted message of
some size, they can infer the size of the plaintext, plus or
minus the cipher block size. From this they may be able to
infer which VDAF is in use and perhaps which task the Client
is uploading reports for. * These attacks can be mitigated by
requiring Clients to submit reports at regular intervals and
independently of whether the event that the task is tracking
has not occurred, so that the absence of reports cannot be
distinguished from their presence.
2. Tampering with network traffic. Attackers may drop messages or
inject new messages into communications between participants.
* DAP mitigates this by using standard HTTP semantics to allow
requests to be retried. However attacks that completely deny
network access to participants are outside of DAP's scope.
[[OPEN ISSUE: The threat model for Prio --- as it's described in the
original paper and [BBCGGI19] --- considers *either* a malicious
Client (attacking robustness) *or* a malicious subset of Aggregators
(attacking privacy). In particular, robustness isn't guaranteed if
any one of the Aggregators is malicious; in theory it may be possible
for a malicious Client and Aggregator to collude and break
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robustness. Is this a contingency we need to address? There are
techniques in [BBCGGI19] that account for this; we need to figure out
if they're practical.]]
7.2. Sybil attacks
Several attacks on privacy involve malicious clients uploading
reports that are valid under the chosen VDAF but incorrect. For
example, a DAP deployment might be measuring the heights of a human
population and configure a VDAF to prove that measurements are values
in the range of 80-250 cm. A malicious Client would not be able to
claim a height of 400 cm, but they could submit multiple bogus
reports inside the acceptable range, which would yield incorrect
averages. More generally, DAP deployments are susceptible to Sybil
attacks [Dou02], especially when carried out by the Leader.
In this type of attack, the adversary adds to a batch a number of
reports that skew the aggregate result in its favor. For example,
sending known measurements to the Aggregators can allow a Collector
to shrink the effective anonymity set by subtracting the known
measurements from the aggregate result. The result may reveal
additional information about the honest measurements, leading to a
privacy violation; or the result may have some property that is
desirable to the adversary ("stats poisoning").
Depending on the deployment and threat model, there are different
ways to address Sybil attacks, including, though not limited to:
1. Authenticating clients, as described in Section 7.2.1;
2. Removing client-specific metadata on individual reports, such as
through the use of anonymizing proxies in the upload flow, as
described in Section 7.3; or
3. Applying differential privacy protections, as described in
Section 7.5.
7.2.1. Client authentication
In settings where it is practical for each Client to have an identity
provisioned (e.g., a user logged into a backend service or a hardware
device programmed with an identity), Client authentication can help
Aggregators (or an authenticating proxy deployed between clients and
the Aggregators; see Section 7.3) ensure that all reports come from
authentic Clients. Note that because the Helper never handles
messages directly from the Clients, reports would have to use an
extension (Section 4.4.3) to convey authentication information to the
Helper.
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However, in some deployments, it will not be practical to require
Clients to authenticate, so Client authentication is not mandatory in
DAP. For example, a widely distributed application that does not
require its users to log in to any service has no obvious way to
authenticate its report uploads.
7.3. Anonymizing proxies
Client reports can contain auxiliary information such as source IP,
HTTP user agent or in deployments which use it, Client authentication
information, which could be used by Aggregators to identify
participating Clients or permit some attacks on robustness. This
auxiliary information could be removed by having Clients submit
reports to an anonymizing proxy server which would then use Oblivious
HTTP [I-D.draft-ietf-ohai-ohttp-08] to forward reports to the DAP
Leader, without requiring any server participating in DAP to be aware
of whatever Client authentication or attestation scheme is in use.
7.4. Task parameters
Selection and distribution of DAP task parameters is out of band from
DAP itself and thus not discussed in this document, but we must
nonetheless discuss the security implications of some task parameter
choices. Generally, attacks involving crafted DAP task parameters
can be mitigated by having the the Aggregators refuse shared
parameters that are trivially insecure (e.g., a minimum batch size of
1 report).
7.4.1. Verification key requirements
The verification key for a task SHOULD be chosen before any reports
are generated so that it is independent of any reports. Moreover, it
SHOULD be fixed for the lifetime of the task and not be rotated. One
way to ensure that the key is independent is to derive the
verification key (verify_key) based on the task ID (task_id) and some
previously agreed upon secret (verify_key_seed) between Aggregators,
as follows:
verify_key = HKDF-Expand(
HKDF-Extract(
"verify_key", # salt
verify_key_seed, # IKM
),
task_id, # info
VERIFY_KEY_SIZE, # L
)
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Here, VERIFY_KEY_SIZE is the length of the verification key, and
HKDF-Extract and HKDF-Expand are as defined in [RFC5869].
This requirement comes from current security analysis for existing
VDAFs. In particular, the security proofs for Prio3 require that the
verification key is chosen independently of the generated reports.
7.4.2. Batch parameters
An important parameter of a DAP deployment is the minimum batch size.
If a batch includes too few reports, then the aggregate result can
reveal information about individual participants. Aggregators must
enforce the agreed-upon minimum batch size during the collect
protocol, but implementations may also opt out of participating in a
DAP task if the minimum batch size is too small. This document does
not specify how to choose minimum batch sizes.
7.4.3. VDAFs and compute requirements
The choice of VDAF can impact the computation required for a DAP
Task. For instance, the Poplar1 VDAF [VDAF] when configured to
compute a set of heavy hitters requires each measurement to be of the
same bit-length which all parties need to agree on prior to VDAF
execution. The computation required for such tasks can increase
superlinearly as multiple rounds of evaluation are needed for each
bit of the measurement value.
When dealing with variable length measurements (e.g domain names), it
is necessary to pad them to convert into fixed-size measurements.
When computing the heavy hitters from a batch of such measurements,
we can early-abort the Poplar1 execution once we have reached the
padding region for a candidate measurement. For smaller length
measurements, this significantly reduces the cost of communication
between Aggregators and the steps required for the computation.
However, malicious Clients can still generate maximum length
measurements forcing the system to always operate at worst-case
performance.
[[TODO: Revisit this paragraph once https://github.com/cfrg/draft-
irtf-cfrg-vdaf/issues/273 is resolved.]]
Therefore, care must be taken that a DAP deployment can comfortably
handle computation of measurements for arbitrarily large sizes,
otherwise, it may result in a DoS possibility for the entire system.
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7.5. Differential privacy
Optionally, DAP deployments can choose to ensure their aggregate
results achieve differential privacy [Vad16]. A simple approach
would require the Aggregators to add two-sided noise (e.g. sampled
from a two-sided geometric distribution) to aggregate shares. Since
each Aggregator is adding noise independently, privacy can be
guaranteed even if all but one of the Aggregators is malicious.
Differential privacy is a strong privacy definition, and protects
users in extreme circumstances: even if an adversary has prior
knowledge of every measurement in a batch except for one, that one
record is still formally protected.
7.6. Robustness in the presence of malicious servers
Most DAP protocols, including Prio and Poplar, are robust against
malicious clients, but are not robust against malicious servers. Any
Aggregator can simply emit bogus aggregate shares and undetectably
spoil aggregates. If enough Aggregators were available, this could
be mitigated by running the protocol multiple times with distinct
subsets of Aggregators chosen so that no Aggregator appears in all
subsets and checking all the aggregate results against each other.
If all the protocol runs do not agree, then participants know that at
least one Aggregator is defective, and it may be possible to identify
the defector (i.e., if a majority of runs agree, and a single
Aggregator appears in every run that disagrees). See #22
(https://github.com/ietf-wg-ppm/draft-ietf-ppm-dap/issues/22) for
discussion.
7.7. Infrastructure diversity
Prio deployments should ensure that Aggregators do not have common
dependencies that would enable a single vendor to reassemble
measurements. For example, if all participating Aggregators stored
unencrypted input shares on the same cloud object storage service,
then that cloud vendor would be able to reassemble all the input
shares and defeat privacy.
7.8. System requirements
7.8.1. Data types
8. IANA Considerations
8.1. Protocol Message Media Types
This specification defines the following protocol messages, along
with their corresponding media types types:
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* HpkeConfigList Section 4.4.1: "application/dap-hpke-config-list"
* Report Section 4.4.2: "application/dap-report"
* AggregationJobInitReq Section 4.5.1.1: "application/dap-
aggregation-job-init-req"
* AggregationJobResp Section 4.5.1.2: "application/dap-aggregation-
job-resp"
* AggregationJobContinueReq Section 4.5.2.1: "application/dap-
aggregation-job-continue-req"
* AggregateShareReq Section 4.6: "application/dap-aggregate-share-
req"
* AggregateShare Section 4.6: "application/dap-aggregate-share"
* CollectionReq Section 4.6: "application/dap-collect-req"
* Collection Section 4.6: "application/dap-collection"
The definition for each media type is in the following subsections.
Protocol message format evolution is supported through the definition
of new formats that are identified by new media types.
IANA [shall update / has updated] the "Media Types" registry at
https://www.iana.org/assignments/media-types with the registration
information in this section for all media types listed above.
[OPEN ISSUE: Solicit review of these allocations from domain
experts.]
8.1.1. "application/dap-hpke-config-list" media type
Type name: application
Subtype name: dap-hpke-config-list
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.2
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Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.2. "application/dap-report" media type
Type name: application
Subtype name: dap-report
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.4.2
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
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Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.3. "application/dap-aggregation-job-init-req" media type
Type name: application
Subtype name: dap-aggregation-job-init-req
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
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Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.4. "application/dap-aggregation-job-resp" media type
Type name: application
Subtype name: dap-aggregation-job-resp
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
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Author: see Authors' Addresses section
Change controller: IESG
8.1.5. "application/dap-aggregation-job-continue-req" media type
Type name: application
Subtype name: dap-aggregation-job-continue-req
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
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8.1.6. "application/dap-aggregate-share-req" media type
Type name: application
Subtype name: dap-aggregate-share-req
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.7. "application/dap-aggregate-share" media type
Type name: application
Subtype name: dap-aggregate-share
Required parameters: N/A
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Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.8. "application/dap-collect-req" media type
Type name: application
Subtype name: dap-collect-req
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
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Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.9. "application/dap-collection" media type
Type name: application
Subtype name: dap-collection
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.6
Interoperability considerations: N/A
Published specification: this specification
Applications that use this media type: N/A
Fragment identifier considerations: N/A
Additional information: Magic number(s): N/A
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Deprecated alias names for this type: N/A
File extension(s): N/A
Macintosh file type code(s): N/A
Person and email address to contact for further information: see Aut
hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.2. DAP Type Registries
This document also requests creation of a new "Distributed
Aggregation Protocol Parameters" page. This page will contain
several new registries, described in the following sections.
8.2.1. Query Types Registry
This document requests creation of a new registry for Query Types.
This registry should contain the following columns:
[TODO: define how we want to structure this registry when the time
comes]
8.2.2. Upload Extension Registry
This document requests creation of a new registry for extensions to
the Upload protocol. This registry should contain the following
columns:
[TODO: define how we want to structure this registry when the time
comes]
8.2.3. Prepare Error Registry
This document requests creation of a new registry for PrepareError
values. This registry should contain the following columns:
Name: The name of the PrepareError value
Value: The 1-byte value of the PrepareError value
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Reference: A reference to where the PrepareError type is defined.
The initial contents of this registry are as defined in
Section 4.5.1.2, with this document as the reference.
8.3. URN Sub-namespace for DAP (urn:ietf:params:ppm:dap)
The following value [will be/has been] registered in the "IETF URN
Sub-namespace for Registered Protocol Parameter Identifiers"
registry, following the template in [RFC3553]:
Registry name: dap
Specification: [[THIS DOCUMENT]]
Repository: http://www.iana.org/assignments/dap
Index value: No transformation needed.
Initial contents: The types and descriptions in the table in
Section 3.2 above, with the Reference field set to point to this
specification.
9. Acknowledgments
The text in Section 3 is based extensively on [RFC8555]
10. References
10.1. Normative References
[HPKE] Barnes, R., Bhargavan, K., Lipp, B., and C. Wood, "Hybrid
Public Key Encryption", RFC 9180, DOI 10.17487/RFC9180,
February 2022, <https://www.rfc-editor.org/rfc/rfc9180>.
[I-D.draft-ietf-ohai-ohttp-08]
Thomson, M. and C. A. Wood, "Oblivious HTTP", Work in
Progress, Internet-Draft, draft-ietf-ohai-ohttp-08, 15
March 2023, <https://datatracker.ietf.org/doc/html/draft-
ietf-ohai-ohttp-08>.
[OAuth2] Hardt, D., Ed., "The OAuth 2.0 Authorization Framework",
RFC 6749, DOI 10.17487/RFC6749, October 2012,
<https://www.rfc-editor.org/rfc/rfc6749>.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC3553] Mealling, M., Masinter, L., Hardie, T., and G. Klyne, "An
IETF URN Sub-namespace for Registered Protocol
Parameters", BCP 73, RFC 3553, DOI 10.17487/RFC3553, June
2003, <https://www.rfc-editor.org/rfc/rfc3553>.
[RFC4648] Josefsson, S., "The Base16, Base32, and Base64 Data
Encodings", RFC 4648, DOI 10.17487/RFC4648, October 2006,
<https://www.rfc-editor.org/rfc/rfc4648>.
[RFC5861] Nottingham, M., "HTTP Cache-Control Extensions for Stale
Content", RFC 5861, DOI 10.17487/RFC5861, May 2010,
<https://www.rfc-editor.org/rfc/rfc5861>.
[RFC7807] Nottingham, M. and E. Wilde, "Problem Details for HTTP
APIs", RFC 7807, DOI 10.17487/RFC7807, March 2016,
<https://www.rfc-editor.org/rfc/rfc7807>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/rfc/rfc8446>.
[RFC9110] Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
Ed., "HTTP Semantics", STD 97, RFC 9110,
DOI 10.17487/RFC9110, June 2022,
<https://www.rfc-editor.org/rfc/rfc9110>.
[RFC9111] Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
Ed., "HTTP Caching", STD 98, RFC 9111,
DOI 10.17487/RFC9111, June 2022,
<https://www.rfc-editor.org/rfc/rfc9111>.
[RFC9457] Nottingham, M., Wilde, E., and S. Dalal, "Problem Details
for HTTP APIs", RFC 9457, DOI 10.17487/RFC9457, July 2023,
<https://www.rfc-editor.org/rfc/rfc9457>.
[SHS] Dang, Q., "Secure Hash Standard", National Institute of
Standards and Technology, DOI 10.6028/nist.fips.180-4,
July 2015, <https://doi.org/10.6028/nist.fips.180-4>.
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[VDAF] Barnes, R., Cook, D., Patton, C., and P. Schoppmann,
"Verifiable Distributed Aggregation Functions", Work in
Progress, Internet-Draft, draft-irtf-cfrg-vdaf-07, 31
August 2023, <https://datatracker.ietf.org/doc/html/draft-
irtf-cfrg-vdaf-07>.
10.2. Informative References
[BBCGGI19] Boneh, D., Boyle, E., Corrigan-Gibbs, H., Gilboa, N., and
Y. Ishai, "Zero-Knowledge Proofs on Secret-Shared Data via
Fully Linear PCPs", 5 January 2021,
<https://eprint.iacr.org/2019/188>.
[BBCGGI21] Boneh, D., Boyle, E., Corrigan-Gibbs, H., Gilboa, N., and
Y. Ishai, "Lightweight Techniques for Private Heavy
Hitters", 5 January 2021,
<https://eprint.iacr.org/2021/017>.
[CGB17] Corrigan-Gibbs, H. and D. Boneh, "Prio: Private, Robust,
and Scalable Computation of Aggregate Statistics", 14
March 2017, <https://crypto.stanford.edu/prio/paper.pdf>.
[Dou02] Douceur, J., "The Sybil Attack", 10 October 2022,
<https://link.springer.com/
chapter/10.1007/3-540-45748-8_24>.
[I-D.draft-dcook-ppm-dap-interop-test-design-04]
Cook, D., "DAP Interoperation Test Design", Work in
Progress, Internet-Draft, draft-dcook-ppm-dap-interop-
test-design-04, 14 June 2023,
<https://datatracker.ietf.org/doc/html/draft-dcook-ppm-
dap-interop-test-design-04>.
[RFC5869] Krawczyk, H. and P. Eronen, "HMAC-based Extract-and-Expand
Key Derivation Function (HKDF)", RFC 5869,
DOI 10.17487/RFC5869, May 2010,
<https://www.rfc-editor.org/rfc/rfc5869>.
[RFC8555] Barnes, R., Hoffman-Andrews, J., McCarney, D., and J.
Kasten, "Automatic Certificate Management Environment
(ACME)", RFC 8555, DOI 10.17487/RFC8555, March 2019,
<https://www.rfc-editor.org/rfc/rfc8555>.
[Vad16] Vadhan, S., "The Complexity of Differential Privacy", 9
August 2016,
<https://privacytools.seas.harvard.edu/files/privacytools/
files/complexityprivacy_1.pdf>.
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Authors' Addresses
Tim Geoghegan
ISRG
Email: timgeog+ietf@gmail.com
Christopher Patton
Cloudflare
Email: chrispatton+ietf@gmail.com
Eric Rescorla
Mozilla
Email: ekr@rtfm.com
Christopher A. Wood
Cloudflare
Email: caw@heapingbits.net
Geoghegan, et al. Expires 25 April 2024 [Page 78]