Distributed Aggregation Protocol for Privacy Preserving Measurement
draft-ietf-ppm-dap-02
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| Authors | Tim Geoghegan , Christopher Patton , Eric Rescorla , Christopher A. Wood | ||
| Last updated | 2022-09-22 | ||
| Replaces | draft-gpew-priv-ppm | ||
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draft-ietf-ppm-dap-02
Network Working Group T. Geoghegan
Internet-Draft ISRG
Intended status: Standards Track C. Patton
Expires: 26 March 2023 Cloudflare
E. Rescorla
Mozilla
C. A. Wood
Cloudflare
22 September 2022
Distributed Aggregation Protocol for Privacy Preserving Measurement
draft-ietf-ppm-dap-02
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 26 March 2023.
Copyright Notice
Copyright (c) 2022 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
Provisions Relating to IETF Documents (https://trustee.ietf.org/
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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 . . . . . . . . . . . . . . . 5
2. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1. System Architecture . . . . . . . . . . . . . . . . . . . 7
2.2. Validating Inputs . . . . . . . . . . . . . . . . . . . . 10
3. Message Transport . . . . . . . . . . . . . . . . . . . . . . 10
3.1. HTTPS Request Authentication . . . . . . . . . . . . . . 11
3.2. Errors . . . . . . . . . . . . . . . . . . . . . . . . . 11
4. Protocol Definition . . . . . . . . . . . . . . . . . . . . . 13
4.1. Queries . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.1.1. Time-interval Queries . . . . . . . . . . . . . . . . 16
4.1.2. Fixed-size Queries . . . . . . . . . . . . . . . . . 16
4.2. Task Configuration . . . . . . . . . . . . . . . . . . . 17
4.3. Uploading Reports . . . . . . . . . . . . . . . . . . . . 18
4.3.1. HPKE Configuration Request . . . . . . . . . . . . . 18
4.3.2. Upload Request . . . . . . . . . . . . . . . . . . . 19
4.3.3. Upload Extensions . . . . . . . . . . . . . . . . . . 22
4.3.4. Upload Message Security . . . . . . . . . . . . . . . 22
4.4. Verifying and Aggregating Reports . . . . . . . . . . . . 23
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4.4.1. Aggregate Initialization . . . . . . . . . . . . . . 24
4.4.2. Aggregate Continuation . . . . . . . . . . . . . . . 31
4.4.3. Aggregate Message Security . . . . . . . . . . . . . 33
4.5. Collecting Results . . . . . . . . . . . . . . . . . . . 34
4.5.1. Collection Initialization . . . . . . . . . . . . . . 34
4.5.2. Collection Aggregation . . . . . . . . . . . . . . . 36
4.5.3. Collection Finalization . . . . . . . . . . . . . . . 39
4.5.4. Aggregate Share Encryption . . . . . . . . . . . . . 39
4.5.5. Collect Message Security . . . . . . . . . . . . . . 40
4.5.6. Batch Validation . . . . . . . . . . . . . . . . . . 40
4.5.7. Anti-replay . . . . . . . . . . . . . . . . . . . . . 42
5. Operational Considerations . . . . . . . . . . . . . . . . . 43
5.1. Protocol participant capabilities . . . . . . . . . . . . 43
5.1.1. Client capabilities . . . . . . . . . . . . . . . . . 43
5.1.2. Aggregator capabilities . . . . . . . . . . . . . . . 43
5.1.3. Collector capabilities . . . . . . . . . . . . . . . 44
5.2. Data resolution limitations . . . . . . . . . . . . . . . 44
5.3. Aggregation utility and soft batch deadlines . . . . . . 45
5.4. Protocol-specific optimizations . . . . . . . . . . . . . 45
5.4.1. Reducing storage requirements . . . . . . . . . . . . 45
6. Compliance Requirements . . . . . . . . . . . . . . . . . . . 46
7. Security Considerations . . . . . . . . . . . . . . . . . . . 46
7.1. Threat model . . . . . . . . . . . . . . . . . . . . . . 47
7.1.1. Client/user . . . . . . . . . . . . . . . . . . . . . 47
7.1.2. Aggregator . . . . . . . . . . . . . . . . . . . . . 48
7.1.3. Leader . . . . . . . . . . . . . . . . . . . . . . . 50
7.1.4. Collector . . . . . . . . . . . . . . . . . . . . . . 51
7.1.5. Aggregator collusion . . . . . . . . . . . . . . . . 51
7.1.6. Attacker on the network . . . . . . . . . . . . . . . 51
7.2. Client authentication or attestation . . . . . . . . . . 52
7.3. Anonymizing proxies . . . . . . . . . . . . . . . . . . . 53
7.4. Batch parameters . . . . . . . . . . . . . . . . . . . . 53
7.5. Differential privacy . . . . . . . . . . . . . . . . . . 53
7.6. Robustness in the presence of malicious servers . . . . . 54
7.7. Infrastructure diversity . . . . . . . . . . . . . . . . 54
7.8. System requirements . . . . . . . . . . . . . . . . . . . 54
7.8.1. Data types . . . . . . . . . . . . . . . . . . . . . 54
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 54
8.1. Protocol Message Media Types . . . . . . . . . . . . . . 54
8.1.1. "application/dap-hpke-config" media type . . . . . . 55
8.1.2. "application/dap-report" media type . . . . . . . . . 56
8.1.3. "application/dap-aggregate-initialize-req" media
type . . . . . . . . . . . . . . . . . . . . . . . . 57
8.1.4. "application/dap-aggregate-initialize-resp" media
type . . . . . . . . . . . . . . . . . . . . . . . . 58
8.1.5. "application/dap-aggregate-continue-req" media
type . . . . . . . . . . . . . . . . . . . . . . . . 58
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8.1.6. "application/dap-aggregate-continue-resp" media
type . . . . . . . . . . . . . . . . . . . . . . . . 59
8.1.7. "application/dap-aggregate-share-req" media type . . 60
8.1.8. "application/dap-aggregate-share-resp" media type . . 61
8.1.9. "application/dap-collect-req" media type . . . . . . 62
8.1.10. "application/dap-collect-req" media type . . . . . . 63
8.2. Query Types Registry . . . . . . . . . . . . . . . . . . 64
8.3. Upload Extension Registry . . . . . . . . . . . . . . . . 64
8.4. URN Sub-namespace for DAP (urn:ietf:params:ppm:dap) . . . 64
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 64
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 64
10.1. Normative References . . . . . . . . . . . . . . . . . . 64
10.2. Informative References . . . . . . . . . . . . . . . . . 66
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 66
1. Introduction
This document describes a distributed aggregation protocol 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.
02:
* 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.)
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* 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.
The following terms are used:
Aggregate result: The output of the aggregation function over a
given set of reports.
Aggregate share: A share of the aggregate result emitted by an
aggregator. Aggregate shares are reassembled by the collector
into the final output.
Aggregation function: The function computed over the users' inputs.
Aggregator: An endpoint that runs the input-validation protocol and
accumulates input shares.
Batch: A set of reports that are aggregated into an output.
Batch duration: The time difference between the oldest and newest
report in a batch.
Batch interval: A parameter of the collect or aggregate-share
request that specifies the time range of the reports in the batch.
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Client: The endpoint from which a user sends data to be aggregated,
e.g., a web browser.
Collector: The endpoint that receives the output of the aggregation
function.
Helper: Executes the protocol as instructed by the leader.
Input: The measurement (or measurements) emitted by a client, before
any encryption or secret sharing scheme is applied.
Input share: An aggregator's share of the output of the VDAF [VDAF]
sharding algorithm. This algorithm is run by each client in order
to cryptographically protect its measurement.
Leader: A distinguished aggregator that coordinates input validation
and data collection.
Measurement: A single value (e.g., a count) being reported by a
client. Multiple measurements may be grouped into a single
protocol input.
Minimum batch duration: The minimum batch duration permitted for a
DAP task, i.e., the minimum time difference between the oldest and
newest report in a batch.
Minimum batch size: The minimum number of reports in a batch.
Output share: An aggregator's share of the output of the VDAF [VDAF]
preparation step. Many output shares are combined into an
aggregate share via the VDAF aggregation algorithm.
Proof: A value generated by the client and used by the aggregators
to verify the client's input.
Report: Uploaded to the leader from the client. A report contains
the secret-shared and encrypted input and proof.
Server: An aggregator.
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].
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2. Overview
The protocol is executed by a large set of clients and a small set of
servers. Servers are referred to as _aggregators_. Each client's
input to the protocol is a set of measurements (e.g., counts of some
user behavior). Given the input set of measurements x_1, ..., x_n
held by n users, the goal of a protocol for privacy preserving
measurement is to compute y = F(p, x_1, ..., x_n) for some function F
while revealing nothing else about the measurements.
This protocol is extensible and allows for the addition of new
cryptographic schemes that implement the VDAF interface specified in
[VDAF]. Candidates include:
* Prio3, which allows for aggregate statistics such as sum, mean,
histograms, etc. This class of VDAFs is based on Prio [CGB17] and
includes improvements described in [BBCGGI19].
* Poplar1, which allows for finding the most popular strings among a
collection 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.
This protocol is designed to work with schemes that use secret
sharing. Rather than sending its input in the clear, each client
shards its measurements into a sequence of _input shares_ and sends
an input share to each of the aggregators. This provides two
important properties:
* It is impossible to deduce the measurement without knowing _all_
of the shares.
* It allows the aggregators to compute the final output by first
aggregating up their measurements shares locally, then combining
the results to obtain the final output.
2.1. System Architecture
The overall system architecture is shown in Figure 1.
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+------------+
| |
+--------+ | Helper |
| | | |
| Client +----+ +-----^------+
| | | |
+--------+ | |
| |
+--------+ | +-----v------+ +-----------+
| | +-----> | | |
| Client +----------> Leader <---------> Collector |
| | +-----> | | |
+--------+ | +-----^------+ +-----------+
| |
+--------+ | |
| | | |
| Client +----+ +-----V------+
| | | |
+--------+ | Helper |
| |
+------------+
Figure 1: System Architecture
[[OPEN ISSUE: This shows two helpers, but the document only allows
one for now. https://github.com/ietf-wg-ppm/draft-ietf-ppm-dap/
issues/117]]
The main participants in the protocol are as follows:
Collector: The entity which wants to take the measurement and
ultimately receives the results. Any given measurement 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 the other aggregators to compute the final
aggregate. This protocol defines two types of aggregators:
Leaders and Helpers. For each measurement, there is a single
leader and helper.
Leader: The leader is responsible for coordinating the protocol. It
receives the encrypted shares, distributes them to the helpers,
and orchestrates the process of computing the final measurement as
requested by the collector.
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Helper: Helpers are responsible for executing the protocol as
instructed by the leader. The protocol is designed so that
helpers can be relatively lightweight, with most of the state held
at the leader.
The basic unit of DAP is the "task" which represents a single
measurement (though potentially taken over multiple time windows).
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 window".
These parameters are distributed out of band to the clients and to
the aggregators. They are distributed by the collecting entity in
some authenticated form. Each task is identified by a unique 32-byte
ID which is used to refer to it in protocol messages.
During the duration of the measurement, each client records its own
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 measurement, the client may
only send one report or may send many reports over time.
The leader distributes the shares to the helpers and orchestrates the
process of verifying them (see Section 2.2) and assembling them into
a final measurement 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.
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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 the values for individual clients.
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 distributed zero-knowledge proof of the input's
validity [BBCGGI19] 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.
3. Message Transport
Communications between DAP participants are carried over HTTPS
[RFC9110]. HTTPS provides server authentication and confidentiality.
Use of HTTPS is REQUIRED.
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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. 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, or TLS client
certificates are another viable solution. This 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 document [RFC7807]. 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:"):
+============================+=====================================+
| Type | Description |
+============================+=====================================+
| unrecognizedMessage | The message type for a response was |
| | incorrect or the payload was |
| | malformed. |
+----------------------------+-------------------------------------+
| unrecognizedTask | An endpoint received a message with |
| | an unknown task ID. |
+----------------------------+-------------------------------------+
| unrecognizedAggregationJob | An endpoint received a message with |
| | an unknown aggregation job ID. |
+----------------------------+-------------------------------------+
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| outdatedConfig | The message was generated using an |
| | outdated configuration. |
+----------------------------+-------------------------------------+
| reportTooLate | Report could not be processed |
| | because it arrived too late. |
+----------------------------+-------------------------------------+
| reportTooEarly | Report could not be processed |
| | because its timestamp is too far in |
| | the future. |
+----------------------------+-------------------------------------+
| batchInvalid | A collect or aggregate-share |
| | request was made with invalid batch |
| | parameters. |
+----------------------------+-------------------------------------+
| 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. |
+----------------------------+-------------------------------------+
| queryMismatch | Query type indicated by a message |
| | does not match the task's query |
| | type. |
+----------------------------+-------------------------------------+
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 8.4). Clients SHOULD display the "detail" field of all
errors. The "instance" value MUST be the endpoint to which the
request was targeted. The problem document MUST also include a
"taskid" member which contains the associated DAP task ID (this value
is always known, see Section 4.2), encoded in Base 64 using the URL
and filename safe alphabet with no padding defined in sections 5 and
3.2 of [RFC4648].
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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 'unrecognizedMessage'" refers to an error
document with "type" value
"urn:ietf:params:ppm:dap:error:unrecognizedMessage".
This document uses the verbs "abort" and "alert with [some error
message]" to describe how protocol participants react to various
error conditions.
4. Protocol Definition
DAP has three major interactions which need to be defined:
* Uploading reports from the client to the aggregators, specified in
Section 4.3
* Computing the results of a given measurement, specified in
Section 4.4
* Collecting aggregated results, specified in Section 4.5
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>;
Duration uint64; /* Number of seconds elapsed between two instants */
Time uint64; /* 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. */
ReportID uint8[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;
4.1. Queries
Aggregated results are computed based on sets of report, 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:
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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 can introduce new query types as needed (see
Section 8.2). 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];
struct {
QueryType query_type;
select (Query.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: BatchID batch_id;
}
} Query;
The parameters pertaining to each query type are described in one of
the subsections below. The query is issued in-band as part of the
collect sub-protocol (Section 4.5). 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
reports and aggregation function.
* time_precision - Clients use this value to truncate their report
timestamps; see Section 4.3. Additional semantics may apply,
depending on the query type. (See Section 4.5.6 for details.)
The parameters pertaining to specific query types are described in
the relevant subsection below.
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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 4.3)
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), (1659545000, 1000), (1659546000, 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.
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).
In addition to the minimum batch size common to all query types, the
configuration includes a "maximum batch size", 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 4.4) 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.
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[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.]
[OPEN ISSUE: It may be desirable to allow Collectors to query for a
current/ recent batch ID. How important this is will be determined
by 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];
A TaskID is a globally unique sequence of bytes. It is RECOMMENDED
that this be set to a random string output by a cryptographically
secure pseudorandom number generator. Each task has the following
parameters associated with it:
* aggregator_endpoints: A list of URLs relative to which an
aggregator's API endpoints can be found. Each endpoint's list
MUST be in the same order. The leader's endpoint MUST be the
first in the list. The order of the encrypted_input_shares in a
Report (see Section 4.3) MUST be the same as the order in which
aggregators appear in this list.
* 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 instance used for the task,
including the type of measurement associated with the task.
In addition, in order to facilitate the aggregation and collect
protocols, each of the aggregators is configured with following
parameters:
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* collector_config: The [HPKE] configuration of the collector
(described in Section 4.3.1); 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 4.4). [OPEN ISSUE: The manner in which this key is
distributed may be relevant to the VDAF's security. See
issue#161.]
Finally, the collector is configured with the HPKE secret key
corresponding to collector_hpke_config.
4.3. Uploading Reports
Clients periodically upload reports to the leader, which then
distributes the individual shares to each helper.
4.3.1. 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, where
[aggregator] is the aggregator's endpoint URL, obtained from the task
parameters. Clients MAY specify a query parameter task_id when
sending an HTTP GET request to
[aggregator]/hpke_config?task_id=[task-id], where [task-id] is the
task ID obtained from the task parameters, encoded in Base 64 with
URL and filename safe alphabet with no padding, as specified in
sections 5 and 3.2 of [RFC4648]. If the aggregator does not
recognize the task ID, then it responds with HTTP status code 404 Not
Found and an error of type 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 a
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 HpkeConfig value:
[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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struct {
HpkeConfigId id;
HpkeKemId kem_id;
HpkeKdfId kdf_id;
HpkeAeadKdfId 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, or support
multiple cipher suites (a la OHTTP/ECH).]
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
configuration; or
* the HPKE configuration specifies a KEM, KDF, or AEAD algorithm the
client does not recognize.
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
key 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.
4.3.2. Upload Request
Clients upload reports by using an HTTP POST to [leader]/upload,
where [leader] is the first entry in the task's aggregator endpoints.
The payload is structured as follows:
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struct {
ReportID report_id;
Time time;
Extension extensions<0..2^16-1>;
} ReportMetadata;
struct {
TaskID task_id;
ReportMetadata metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext encrypted_input_shares<1..2^32-1>;
} Report;
This message is called the Client's report. It consists of the task
ID, report metadata, the "public share" output by the VDAF's input-
distribution algorithm, and the encrypted input share of each of the
Aggregators. (Note that the public share might be empty, depending
on the VDAF. For example, Prio3 has an empty public share, but
Poplar1 does not. See [VDAF].) The header consists of the task ID
and report "metadata". The metadata consists of the following
fields:
* A report ID used by the Aggregators to ensure the report appears
in at most one batch. (See Section 4.5.7.) The Client MUST
generate this by generating 16 random bytes using a
cryptographically secure random number generator.
* A timestamp representing the time at which the report was
generated. Specifically, the time field is set to the number of
seconds elapsed since the start of the UNIX epoch. The client
SHOULD round this value down to the nearest multiple of
time_precision in order to ensure that that the timestamp cannot
be used to link a report back to the Client that generated it.
* A list of extensions to be included with the report. (See
Section 4.3.3.)
To generate a report, the Client first shards its measurement into
input shares as specified by the VDAF. It then encrypts each input
share as follows:
enc, payload = SealBase(pk,
"dap-02 input share" || 0x01 || server_role,
task_id || metadata || public_share, input_share)
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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),
task_id is the task ID, metadata is the report metadata, and
input_share is the Aggregator's input share.
The order of the encrypted input shares appear MUST match the order
of the task's aggregator_endpoints. That is, the first share should
be the leader's, the second share should be for the first helper, and
so on.
The leader responds to well-formed requests to [leader]/upload with
HTTP status code 200 OK and an empty body. 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 200 OK and an empty body.
The leader responds to requests whose leader encrypted input share
uses an out-of-date HpkeConfig.id value, indicated by
HpkeCiphertext.config_id, with HTTP status code 400 Bad Request and
an error of type 'outdatedConfig'. Clients SHOULD invalidate any
cached aggregator HpkeConfig and retry with a freshly generated
Report. If this retried report does not succeed, clients MUST abort
and discontinue retrying.
The Leader MUST ignore any report pertaining to a batch that has
already been collected. (See Section 4.4.1.4 for details.)
Otherwise, comparing the aggregate result to the previous aggregate
result may result in a privacy violation. (Note that the Helpers
enforce this as well.) The Leader MAY ignore any reports whose
timestamp is past the task's task_expiration. When it does so, the
leader SHOULD abort the upload protocol and alert the client with
error "reportTooLate". Client MAY choose to opt out of the task if
its own clock has passed task_expiration.
Leaders can buffer reports while waiting to aggregate them. 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".
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4.3.3. Upload Extensions
Each Report carries a list of extensions that clients may use to
convey additional, authenticated information in the report. [OPEN
ISSUE: The extensions aren't authenticated. It's probably a good
idea to be a bit more clear about how we envision extensions being
used. Right now this includes client attestation for defeating Sybil
attacks. See issue#89.] 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;
"extension_type" indicates the type of extension, and
"extension_data" contains information specific to the extension.
4.3.4. Upload Message Security
The contents of each input share must be kept confidential from
everyone but the client and the aggregator it is being sent to. In
addition, clients must be able to authenticate the aggregator they
upload to.
HTTPS provides confidentiality between the DAP client and the leader,
but this is not sufficient since the helper's report shares are
relayed through the leader. Confidentiality of report shares is
achieved by encrypting each report share to a public key held by the
respective aggregator using [HPKE]. Clients fetch the public keys
from each aggregator over HTTPS, allowing them to authenticate the
server.
Aggregators MAY require clients to authenticate when uploading
reports. This is an effective mitigation against Sybil [Dou02]
attacks in deployments where it is practical for each client to have
an identity provisioned (e.g., a user logged into an online service
or a hardware device programmed with an identity). If it is used,
client authentication MUST use a scheme that meets the requirements
in Section 3.1.
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In some deployments, it will not be practical to require clients to
authenticate (e.g., a widely distributed application that does not
require its users to login to any service), so client authentication
is not mandatory in DAP.
[[OPEN ISSUE: deployments that don't have client auth will need to do
something about Sybil attacks. Is there any useful guidance or
SHOULD we can provide? Sort of relevant: issue #89]]
4.4. Verifying and Aggregating Reports
Once a set of clients have uploaded their reports to the leader, the
leader can send them to the helpers to be verified and aggregated.
In order to enable the system to handle very large batches of
reports, this process can be performed incrementally. Verification
of a set of reports is referred to as an aggregation job. Each
aggregation job is associated with exactly one DAP task, and a DAP
task can have many aggregation jobs. Each job is associated with an
ID that is unique within the context of a DAP task in order to
distinguish different jobs from one another. Each aggregator uses
this ID as an index into per-job storage, e.g., to keep track of
report shares that belong to a given aggregation job.
To run an aggregation job, the leader sends a message to each helper
containing the report shares in the job. The helper then processes
them (verifying the proofs and incorporating their values into the
ongoing aggregate) and replies to the leader.
The exact structure of the aggregation job flow depends on the VDAF.
Specifically:
* Some VDAFs (e.g., Prio3) allow the leader to start aggregating
reports proactively before all the reports in a batch are
received. Others (e.g., Poplar1) require all the reports to be
present and must be initiated by the collector.
* Processing the reports -- especially validating them -- may
require multiple round trips.
Note that it is possible to aggregate reports from one batch while
reports from the next batch are coming in. This is because each
report is validated independently.
This process is illustrated below in Figure 2. In this example, the
batch size is 20, but the leader opts to process the reports in sub-
batches of 10. Each sub-batch takes two round-trips to process.
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Leader Helper
Aggregate request (Reports 1-10, Job = N) ---------------> \
<----------------------------- Aggregate response (Job = N) | Reports
Aggregate request (continued, Job = N) ------------------> | 1-10
<----------------------------- Aggregate response (Job = N) /
Aggregate request (Reports 11-20, Job = M) --------------> \
<----------------------------- Aggregate response (Job = M) | Reports
Aggregate request (continued, Job = M) ------------------> | 11-20
<----------------------------- Aggregate response (Job = M) /
Figure 2: Aggregation Flow (batch size=20). Multiple aggregation
flows can be executed at the same time.
[OPEN ISSUE: Should there be an indication of whether a given
aggregate request is a continuation of a previous sub-batch?]
The aggregation flow can be thought of as having three phases for
transforming each valid input report share into an output share:
* Initialization: Begin the aggregation flow by sharing report
shares with each helper. Each aggregator, including the leader,
initializes the underlying VDAF instance using these report shares
and the VDAF configured for the corresponding measurement task.
* Continuation: Continue the aggregation flow by exchanging messages
produced by the underlying VDAF instance until aggregation
completes or an error occurs. These messages do not replay the
shares.
* Completion: Finish the aggregate flow, yielding an output share
corresponding for each input report share in the batch.
4.4.1. Aggregate Initialization
The leader begins an aggregation job by choosing a set of candidate
reports that pertain to the same DAP task and a unique job ID. The
job ID is a 32-byte value, structured as follows:
opaque AggregationJobID[32];
The leader can run this process for many candidate reports in
parallel as needed. After choosing the set of candidates, the leader
begins aggregation by splitting each report into "report shares", one
for each aggregator. The leader and helpers then run the aggregate
initialization flow to accomplish two tasks:
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1. Recover and determine which input report shares are invalid.
2. For each valid report share, initialize the VDAF preparation
process.
An invalid report share is marked with one of the following errors:
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),
(255)
} ReportShareError;
The leader and helper initialization behavior is detailed below.
4.4.1.1. Leader Initialization
The leader begins the aggregate initialization phase with the set of
candidate report shares as follows:
1. Generate a fresh AggregationJobID. This ID MUST be unique within
the context of the corresponding DAP task. It is RECOMMENDED
that this be set to a random string output by a cryptographically
secure pseudorandom number generator.
2. Decrypt the input share for each report share as described in
Section 4.4.1.3.
3. Check that the resulting input share is valid as described in
Section 4.4.1.4.
4. Initialize VDAF preparation as described in Section 4.4.1.5.
If any step yields an invalid report share, the leader removes the
report share from the set of candidate reports. Once the leader has
initialized this state for all valid candidate report shares, it then
creates an AggregateInitializeReq message for each helper to
initialize the preparation of this candidate set. The
AggregateInitializeReq message is structured as follows:
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struct {
ReportMetadata metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext encrypted_input_share;
} ReportShare;
struct {
QueryType query_type;
select (PartialBatchSelector.query_type) {
case time_interval: Empty;
case fixed_size: BatchID batch_id;
};
} PartialBatchSelector;
struct {
TaskID task_id;
AggregationJobID job_id;
opaque agg_param<0..2^16-1>;
PartialBatchSelector part_batch_selector;
ReportShare report_shares<1..2^32-1>;
} AggregateInitializeReq;
[[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:
* The ID of the task for which the reports will be aggregated.
* The aggregation job ID.
* The opaque, VDAF-specific aggregation parameter provided during
the collection flow (agg_param),
[[OPEN ISSUE: Check that this handling of agg_param is appropriate
when the definition of Poplar is done.]]
* Information 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.
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[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
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 "queryMismatch".
* The sequence of report shares to aggregate. The
encrypted_input_share field of the report share is the
HpkeCiphertext whose index in Report.encrypted_input_shares is
equal to the index of the aggregator in the task's
aggregator_endpoints to which the AggregateInitializeReq is being
sent.
Let [aggregator] denote the helper's API endpoint. The leader sends
a POST request to [aggregator]/aggregate with its
AggregateInitializeReq message as the payload. The media type is
"message/dap-aggregate-initialize-req".
4.4.1.2. Helper Initialization
Each helper begins their portion of the aggregate initialization
phase with the set of candidate report shares obtained in an
AggregateInitializeReq message from the leader. It attempts to
recover and validate the corresponding input shares similar to the
leader, and eventually returns a response to the leader carrying a
VDAF-specific message for each report share.
To begin this process, the helper first checks that the report IDs in
AggregateInitializeReq.report_shares are all distinct. If two
ReportShare values have the same report ID, then the helper MUST
abort with error "unrecognizedMessage". If this check succeeds, the
helper then attempts to recover each input share in
AggregateInitializeReq.report_shares as follows:
1. Decrypt the input share for each report share as described in
Section 4.4.1.3.
2. Check that the resulting input share is valid as described in
Section 4.4.1.4.
3. Initialize VDAF preparation and initial outputs as described in
Section 4.4.1.5.
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[[OPEN ISSUE: consider moving the helper report ID check into
Section 4.4.1.4]]
Once the helper has processed each valid report share in
AggregateInitializeReq.report_shares, the helper then creates an
AggregateInitializeResp message to complete its initialization. This
message is structured as follows:
enum {
continued(0),
finished(1),
failed(2),
(255)
} PrepareStepResult;
struct {
ReportID report_id;
PrepareStepResult prepare_step_result;
select (PrepareStep.prepare_step_result) {
case continued: opaque prep_msg<0..2^32-1>; /* VDAF preparation message */
case finished: Empty;
case failed: ReportShareError;
};
} PrepareStep;
struct {
PrepareStep prepare_steps<1..2^32-1>;
} AggregateInitializeResp;
The message is a sequence of PrepareStep values, the order of which
matches that of the ReportShare values in
AggregateInitializeReq.report_shares. Each report that was marked as
invalid is assigned the PrepareStepResult failed. Otherwise, the
PrepareStep is either marked as continued with the output prep_msg,
or is marked as finished if the VDAF preparation process is finished
for the report share.
The helper's response to the leader is an HTTP status code 200 OK
whose body is the AggregateInitializeResp and media type is "message/
dap-aggregate-initialize-resp".
Upon receipt of a helper's AggregateInitializeResp message, the
leader checks that the sequence of PrepareStep messages corresponds
to the ReportShare sequence of the AggregateInitializeReq. If any
message appears out of order, is missing, has an unrecognized report
ID, or if two messages have the same report ID, then the leader MUST
abort with error "unrecognizedMessage".
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[[OPEN ISSUE: the leader behavior here is sort of bizarre -- to whom
does it abort?]]
4.4.1.3. Input Share Decryption
Each report share has a corresponding task ID, report metadata
(report ID, timestamp, and extensions), the public share sent to each
Aggregator, and the recipient's encrypted input share. Let task_id,
metadata, public_share, and encrypted_input_share denote these
values, respectively. Given these values, an aggregator decrypts the
input share as follows. First, the aggregator looks up the HPKE
config and corresponding secret key indicated by
encrypted_input_share.config_id. If not found, then it marks the
report share as invalid with the error hpke_unknown_config_id.
Otherwise, it decrypts the payload with the following procedure:
input_share = OpenBase(encrypted_input_share.enc, sk,
"dap-02 input share" || 0x01 || server_role,
task_id || metadata || public_share,
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). If
decryption fails, the aggregator marks the report share as invalid
with the error hpke_decrypt_error. Otherwise, it outputs the
resulting input_share.
4.4.1.4. Early 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
ReportShareError error.
Before beginning the preparation step, Aggregators are required to
perform the following generic checks.
1. Check if the report has never been aggregated but is contained by
a batch that has been collected. If this check fails, the input
share MUST be marked as invalid with the error batch_collected.
This prevents additional reports from being aggregated after its
batch has already been collected.
2. Check if the report has already been aggregated with this
aggregation parameter. If this check fails, the input share MUST
be marked as invalid with the error report_replayed. This is the
case if the report was used in a previous aggregate request and
is therefore a replay.
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3. 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 4.5.6.) If both
checks succeed, the input share is not marked as invalid.
4. Check if the report's timestamp has passed its task's
task_expiration time, if so the Aggregator MAY mark the input
share as invalid with the error "task_expired".
5. Finally, if an Aggregator cannot determine if an input share is
valid. it MUST mark the input share as invalid with error
report_dropped. This situation arises if, for example, the
Aggregator has evicted from long-term storage the state required
to perform the check. (See Section 5.4.1 for details.)
If all of the above checks succeed, the input share is not marked as
invalid.
4.4.1.5. Input Share Preparation
Input share preparation consists of running the preparation-state
initialization algorithm for the VDAF associated with the task and
computes the first state transition. This produces three possible
values:
1. An error, in which case the input report share is marked as
invalid.
2. An output share, in which case the aggregator stores the output
share for future collection as described in Section 4.5.
3. An initial VDAF state and preparation message, denoted
(prep_state, prep_msg).
Each aggregator runs this procedure for a given input share with
corresponding report ID as follows:
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prep_state = VDAF.prep_init(vdaf_verify_key,
agg_id,
agg_param,
report_id,
public_share,
input_share)
out = VDAF.prep_next(prep_state, None)
vdaf_verify_key is the VDAF verification key shared by the
aggregators; agg_id is the aggregator ID (0x00 for the Leader and
0x01 for the helper); agg_param is the opaque aggregation parameter
distributed to the aggregators by the collector; public_share is the
public share generated by the client and distributed to each
aggregator; and input_share is the aggregator's input share.
If either step fails, the aggregator marks the report as invalid with
error vdaf_prep_error. Otherwise, the value out is interpreted as
follows. If this is the last round of the VDAF, then out is the
aggregator's output share. Otherwise, out is the pair (prep_state,
prep_msg).
4.4.2. Aggregate Continuation
In the continuation phase, the leader drives the VDAF preparation of
each share in the candidate report set until the underlying VDAF
moves into a terminal state, yielding an output share for all
aggregators or an error. This phase may involve multiple rounds of
interaction depending on the underlying VDAF. Each round trip is
initiated by the leader.
4.4.2.1. Leader Continuation
The leader begins each round of continuation for a report share based
on its locally computed prepare message and the previous PrepareStep
from the helper. If PrepareStep is of type "failed", then the leader
marks the report as failed and removes it from the candidate report
set and does not process it further. If the type is "finished", then
the leader aborts with "unrecognizedMessage". [[OPEN ISSUE: This
behavior is not specified.]] If the type is "continued", then the
leader proceeds as follows.
Let leader_outbound denote the leader's prepare message and
helper_outbound denote the helper's. The leader computes the next
state transition as follows:
inbound = VDAF.prep_shares_to_prep(agg_param, [leader_outbound, helper_outbound])
out = VDAF.prep_next(prep_state, inbound)
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where [leader_outbound, helper_outbound] is a vector of two elements.
If either of these operations fails, then the leader marks the report
as invalid. Otherwise it interprets out as follows: If this is the
last round of the VDAF, then out is the aggregator's output share, in
which case the aggregator finishes and stores its output share for
further processing as described in Section 4.5. Otherwise, out is
the pair (new_state, prep_msg), where new_state is its updated state
and prep_msg is its next VDAF message (which will be leader_outbound
in the next round of continuation). For the latter case, the helper
sets prep_state to new_state.
The leader then sends each PrepareStep to the helper in an
AggregateContinueReq message, structured as follows:
struct {
TaskID task_id;
AggregationJobID job_id;
PrepareStep prepare_steps<1..2^32-1>;
} AggregateContinueReq;
For each aggregator endpoint [aggregator] in
AggregateContinueReq.task_id's parameters except its own, the leader
sends a POST request to [aggregator]/aggregate with
AggregateContinueReq as the payload and the media type set to
"message/dap-aggregate-continue-req".
4.4.2.2. Helper Continuation
The helper continues with preparation for a report share by combining
the leader's input message in AggregateContinueReq and its current
preparation state (prep_state). This step yields one of three
outputs:
1. An error, in which case the input report share is marked as
invalid.
2. An output share, in which case the helper stores the output share
for future collection as described in Section 4.5.
3. An updated VDAF state and preparation message, denoted
(prep_state, prep_msg).
To carry out this step, for each PrepareStep in
AggregateContinueReq.prepare_steps received from the leader, the
helper performs the following check to determine if the report share
should continue being prepared:
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* If failed, then mark the report as failed and reply with a failed
PrepareStep to the leader.
* If finished, then mark the report as finished and reply with a
finished PrepareStep to the leader. The helper then stores the
output share and awaits for collection; see Section 4.5.
Otherwise, preparation continues. In this case, the helper computes
its updated state and output message as follows:
out = VDAF.prep_next(prep_state, inbound)
where inbound is the previous VDAF prepare message sent by the leader
and prep_state is the helper's current preparation state. If this
operation fails, then the helper fails with error vdaf_prep_error.
Otherwise, it interprets out as follows:
* If this is the last round of VDAF preparation phase, then out is
the helper's output share, in which case the helper stores the
output share for future collection.
* Otherwise, the helper interprets out as the tuple (new_state,
prep_msg), where new_state is its updated preparation state and
prep_msg is its next VDAF message.
This output message for each report in
AggregateContinueReq.prepare_steps is then sent to the leader in an
AggregateContinueResp message, structured as follows:
struct {
PrepareStep prepare_steps<1..2^32-1>;
} AggregateContinueResp;
The order of AggregateContinueResp.prepare_steps MUST match that of
the PrepareStep values in AggregateContinueReq.prepare_steps. The
helper's response to the leader is an HTTP status code 200 OK whose
body is the AggregateContinueResp and media type is "message/dap-
aggregate-continue-resp". The helper then awaits the next message
from the leader.
[[OPEN ISSUE: consider relaxing this ordering constraint. See
issue#217.]]
4.4.3. Aggregate Message Security
Aggregate sub-protocol messages must be confidential and mutually
authenticated.
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The aggregate sub-protocol is driven by the leader acting as an HTTPS
client, making requests to the helper's HTTPS server. HTTPS provides
confidentiality and authenticates the helper to the leader.
Leaders MUST authenticate their requests to helpers using a scheme
that meets the requirements in Section 3.1.
4.5. 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 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 4.5.1
2. Aggregate share request and response between the leader and each
aggregator, specified in Section 4.5.2
Once complete, the collector computes the final aggregate result as
specified in Section 4.5.3.
4.5.1. Collection Initialization
To initiate collection, the collector issues a POST request to
[leader]/collect, where [leader] is the leader's endpoint URL. The
body of the request is structured as follows:
[OPEN ISSUE: Decide if and how the collector's request is
authenticated. If not, then we need to ensure that collect job URIs
are resistant to enumeration attacks.]
struct {
TaskID task_id;
Query query;
opaque agg_param<0..2^16-1>; /* VDAF aggregation parameter */
} CollectReq;
The named parameters are:
* task_id, the DAP task ID.
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* query, the Collector's query. The indicated query type MUST match
the task's query type. Otherwise, the Leader MUST abort with
error "queryMismatch".
* agg_param, an aggregation parameter for the VDAF being executed.
This is the same value as in AggregateInitializeReq (see
Section 4.4.1.1).
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, but this cannot be guaranteed. In fact, for some
VDAFs, it is not be possible to begin preparing inputs until the
collector provides the aggregation parameter in the CollectReq. For
these reasons, collect requests are handled asynchronously.
Upon receipt of a CollectReq, the leader begins by checking that the
request meets the requirements of the batch parameters using the
procedure in Section 4.5.6. If so, it immediately sends the
collector a response with HTTP status 303 See Other and a Location
header containing a URI identifying the collect job that can be
polled by the collector, called the "collect job URI".
The leader then begins working with the helper to prepare the shares
satisfying the query (or continues this process, depending on the
VDAF) as described in Section 4.4.
After receiving the response to its CollectReq, the collector makes
an HTTP GET request to the collect job URI to check on the status of
the collect job and eventually obtain the result. If the collect 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 pulling interval to the collector.
If the leader has not yet obtained an aggregator share from each
aggregator, the leader invokes the aggregate share request flow
described in Section 4.5.2. Otherwise, when all aggregator shares
are successfully obtained, the leader responds to subsequent HTTP GET
requests to the collect job's URI with HTTP status code 200 OK and a
body consisting of a CollectResp:
struct {
PartialBatchSelector part_batch_selector;
uint64 report_count;
HpkeCiphertext encrypted_agg_shares<1..2^32-1>;
} CollectResp;
This structure includes the following:
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* 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?]
* The number of reports included in the batch.
* The vector of encrypted aggregate shares. They MUST appear in the
same order as the aggregator endpoints list of the task
parameters.
If obtaining aggregate shares fails, then the leader responds to
subsequent HTTP GET requests to the collect job URI with an HTTP
error status and a problem document as described in Section 3.2.
The collector can send an HTTP DELETE request to the collect job URI,
to which the leader MUST respond with HTTP status 204 No Content.
The leader MAY respond with HTTP status 204 No Content for requests
to a collect job URI which has not received a DELETE request, for
example if the results have been deleted due to age. The leader MUST
respond to subsequent requests to the collect job URI with HTTP
status 204 No Content.
[OPEN ISSUE: Describe how intra-protocol errors yield collect errors
(see issue#57). For example, how does a leader respond to a collect
request if the helper drops out?]
4.5.2. Collection Aggregation
The leader obtains each helper's encrypted aggregate share in order
to respond to the collector's collect response. To do this, the
leader first computes a checksum over the set of output shares
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, for each aggregator endpoint [aggregator] in the parameters
associated with CollectReq.task_id (see Section 4.5) except its own,
the leader sends a POST request to [aggregator]/aggregate_share with
the following message:
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struct {
QueryType query_type;
select (BatchSelector.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: BatchID batch_id;
};
} BatchSelector;
struct {
TaskID task_id;
BatchSelector batch_selector;
opaque agg_param<0..2^16-1>;
uint64 report_count;
opaque checksum[32];
} AggregateShareReq;
The message contains the following parameters:
* The task ID.
* 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 "queryMismatch".
* The opaque aggregation parameter for the VDAF being executed.
This value MUST match the same value in the the
AggregateInitializeReq message sent in at least one run of the
aggregate sub-protocol. (See Section 4.4.1.1). and in CollectReq
(see Section 4.5.1).
* The number number of reports included in the batch.
* The batch checksum.
To handle the leader's request, the helper first ensures that the
request meets the requirements for batch parameters following the
procedure in Section 4.5.6.
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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.out_shares_to_agg_share(agg_param, out_shares)
Note that 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 4.5.6 so that the aggregator knows which
aggregate share to update.
The helper then encrypts agg_share under the collector's HPKE public
key as described in Section 4.5.4, 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 AggregateShareResp:
struct {
HpkeCiphertext encrypted_aggregate_share;
} AggregateShareResp;
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.
After receiving the helper's response, the leader uses the
HpkeCiphertext to respond to a collect request (see Section 4.5).
After issuing an aggregate-share request for 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 helpers as described Section 4.4.1.
Before completing the collect request, 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 it under the collector's HPKE public key as described in
Section 4.5.4.
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4.5.3. Collection Finalization
Once the collector has received a successful collect response from
the leader, it can decrypt the aggregate shares and produce an
aggregate result. The collector decrypts each aggregate share as
described in Section 4.5.4. If the collector successfully decrypts
all aggregate shares, the collector then unshards the aggregate
shares into an aggregate result using the VDAF's agg_shares_to_result
algorithm. In particular, let agg_shares denote the ordered sequence
of aggregator shares, ordered by aggregator index, 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.agg_shares_to_result(agg_param,
agg_shares,
report_count)
4.5.4. Aggregate Share Encryption
Encrypting an aggregate share agg_share for a given AggregateShareReq
is done as follows:
enc, payload = SealBase(pk, "dap-02 aggregate share" || server_role || 0x00,
AggregateShareReq.task_id || AggregateShareReq.batch_selector, agg_share)
where pk is the HPKE public key encoded by the collector's HPKE key,
server_role is 0x02 for the leader and 0x03 for a helper.
The collector decrypts these aggregate shares using the opposite
process. Specifically, given an encrypted input share, denoted
enc_share, for a given batch selector, denoted batch_selector,
decryption works as follows:
agg_share = OpenBase(enc_share.enc, sk, "dap-02 aggregate share" ||
server_role || 0x00, task_id || batch_selector, enc_share.payload)
where sk is the HPKE secret key, task_id is the task ID for the
collect request, and server_role is the role of the server that sent
the aggregate share (0x02 for the leader and 0x03 for the helper).
The value of batch_selector is computed by the Collector from its
query and the response to its query:
* 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.
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4.5.5. Collect Message Security
Collect sub-protocol messages must be confidential and mutually
authenticated.
HTTPS provides confidentiality and authenticates the leader to the
collector. Additionally, the leader encrypts its aggregate share to
a public key held by the collector using [HPKE].
Collectors MUST authenticate their requests to leaders using a scheme
that meets the requirements in Section 3.1.
[[OPEN ISSUE: collector public key is currently in the task
parameters, but this will have to change #102]]
The collector and helper never directly communicate with each other,
but the helper does transmit an aggregate share to the collector
through the leader, as detailed in Section 4.5.2. The aggregate
share must be confidential from everyone but the helper and the
collector.
Confidentiality is achieved by having the helper encrypt its
aggregate share to a public key held by the collector using [HPKE].
There is no authentication between the collector and the helper.
This allows the leader to:
* Send collect parameters to the helper that do not reflect the
parameters chosen by the collector
* 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, which puts these malicious-leader
attacks out of scope (see Section 7).
[[OPEN ISSUE: Should we have authentication in either direction
between the helper and the collector? #155]]
4.5.6. Batch Validation
Before an Aggregator responds to a CollectReq or AggregateShareReq,
it must first check that the request does not violate the parameters
associated with the DAP task. It does so as described here.
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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 the Aggregator MUST abort with error of type "invalidBatchSize".
Next, the Aggregator checks that the batch has not been aggregated
too many times. This is determined by the maximum number of times a
batch can be queried, max_batch_query_count. Unless the query has
been issued less than max_batch_query_count times, the Aggregator
MUST abort with error of type "batchQueriedTooManyTimes".
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.]]
4.5.6.1. Time-interval Queries
4.5.6.1.1. 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.
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4.5.6.1.2. 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 in the batch.
4.5.6.2. Fixed-size Queries
4.5.6.2.1. 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 an AggregateShareReq, the Helper checks that the batch ID
provided by the Leader in its corresponds to a batch ID used in a
previous AggregateInitializeReq for the task.
4.5.6.2.2. 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 in the batch.
4.5.7. Anti-replay
Using a client-provided report multiple times within a single batch,
or using the same report in multiple batches, may allow a server to
learn information about the client's measurement, violating the
privacy goal of DAP. To prevent such replay attacks, this
specification requires the aggregators to detect and filter out
replayed reports.
To detect replay attacks, each aggregator keeps track of the set of
report IDs pertaining to reports that were previously aggregated for
a given task. If the leader receives a report from a client whose
report ID is in this set, it either ignores it or aborts the upload
sub-protocol as described in Section 4.3. A Helper who receives an
encrypted input share whose report ID is in this set rejects the
report as described in Section 4.4.1.4.
[OPEN ISSUE: This has the potential to require aggregators to store
report ID sets indefinitely. See issue#180.]
A malicious aggregator may attempt to force a replay by replacing the
report ID generated by the client with a report ID its peer has not
yet seen. To prevent this, clients incorporate the report ID into
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the AAD for HPKE encryption, ensuring that the output share is only
recovered if the aggregator is given the correct report ID. (See
Section 4.3.2.)
Aggregators prevent the same report from being used in multiple
batches (except as required by the protocol) by only responding to
valid collect requests, as described in Section 4.5.6.
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
Helpers and leaders 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
* Publish and manage an HPKE configuration that can be used for the
upload protocol.
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In addition, for each DAP task, helpers are 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 4.5.7.
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, leaders are required to:
* Implement and store state for the form of inter- and intra-batch
replay mitigation in Section 4.5.7.
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.
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.
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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
parameters. (See Section 4.5.6.)
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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 4.4.1.4. 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.
Currently, the specification does not achieve these goals. In
particular, there are several open issues that need to be addressed
before these goals are met. Details for each issue are below.
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1. When crafted maliciously, collect requests may leak more
information about the measurements than the system intends. For
example, the spec currently allows sequences of collect requests
to reveal an aggregate result for a batch smaller than the
minimum batch size. [OPEN ISSUE: See issue#195. This also has
implications for how we solve issue#183.]
2. 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. Note that this leakage can be
mitigated using differential privacy. [OPEN ISSUE: We have yet
not specified how to add DP.]
3. The core DAP spec does not defend against Sybil attacks. 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: 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"). The upload sub-protocol includes an extensions
mechanism that can be used to prevent --- or at least mitigate
--- these types of attacks. See Section 4.3.3. [OPEN ISSUE: No
such extension has been implemented, so we're not yet sure if the
current mechanism is sufficient.]
7.1. Threat model
[OPEN ISSUE: This subsection is a bit out-of-date.]
In this section, we enumerate the actors participating in the Prio
system and 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
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1. Unshared inputs. Clients are the only actor that can ever see
the original inputs.
2. Unencrypted input shares.
7.1.1.2. Capabilities
1. Individual users can reveal their own input and compromise their
own privacy.
2. Clients (that is, software which might be used by many users of
the system) can defeat privacy by leaking input outside of the
Prio system.
3. Clients may affect the quality of aggregations by reporting false
input.
* Prio can only prove that submitted input is valid, not that it
is true. False input can be mitigated orthogonally to the
Prio protocol (e.g., by requiring that aggregations include a
minimum number of contributions) and so these attacks are
considered to be outside of the threat model.
4. Clients can send invalid encodings of input.
7.1.1.3. Mitigations
1. The input validation protocol executed by the aggregators
prevents either individual clients or a coalition of clients from
compromising the robustness property.
2. If aggregator output satisfies differential privacy Section 7.5,
then all records not leaked by malicious clients are still
protected.
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.
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7.1.2.2. Capabilities
1. Aggregators may defeat the robustness of the system by emitting
bogus output shares.
2. If clients reveal identifying information to aggregators (such as
a trusted identity during client authentication), aggregators can
learn which clients are contributing input.
1. Aggregators may reveal that a particular client contributed
input.
2. Aggregators may attack robustness by selectively omitting
inputs from certain clients.
* For example, omitting submissions from a particular
geographic region to falsely suggest that a particular
localization is not being used.
3. Individual aggregators may compromise availability of the system
by refusing to emit aggregate shares.
4. Input validity proof forging. Any aggregator can collude with a
malicious client to craft a proof that will fool honest
aggregators into accepting invalid input.
5. Aggregators 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.
7.1.2.3. Mitigations
1. The linear secret sharing scheme employed by the client ensures
that privacy is preserved as long as at least one aggregator does
not reveal its input shares.
2. If computed over a sufficient number of reports, aggregate shares
reveal nothing about either the inputs or the participating
clients.
3. Clients can ensure that aggregate counts are non-sensitive by
generating input independently of user behavior. For example, a
client should periodically upload a report even if the event that
the task is tracking has not occurred, so that the absence of
reports cannot be distinguished from their presence.
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4. Bogus inputs can be generated that encode "null" shares that do
not affect the aggregate output, but mask the total number of
true inputs.
* Either leaders or clients can generate these inputs to mask
the total number from non-leader aggregators or all the
aggregators, respectively.
* In either case, care must be taken to ensure that bogus inputs
are indistinguishable from true inputs (metadata, etc),
especially when constructing timestamps on reports.
[OPEN ISSUE: Define what "null" shares are. They should be defined
such that inserting null shares into an aggregation is effectively a
no-op. See issue#98.]
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
1. Input validity proof verification. The leader can forge proofs
and collude with a malicious client to trick aggregators into
aggregating invalid inputs.
* This capability is no stronger than any aggregator's ability
to forge validity proof in collusion with a malicious client.
2. Relaying messages between aggregators. The leader can compromise
availability by dropping messages.
* This capability is no stronger than any aggregator's ability
to refuse to emit aggregate shares.
3. Shrinking the anonymity set. The leader instructs aggregators to
construct output parts and so could request aggregations over few
inputs.
7.1.3.2. Mitigations
1. Aggregators enforce agreed upon minimum aggregation thresholds to
prevent deanonymizing.
2. If aggregator output satisfies differential privacy Section 7.5,
then genuine records are protected regardless of the size of the
anonymity set.
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7.1.4. Collector
7.1.4.1. Capabilities
1. Advertising shared configuration parameters (e.g., minimum
thresholds for aggregations, joint randomness, arithmetic
circuits).
2. Collectors may trivially defeat availability by discarding
aggregate shares submitted by aggregators.
3. Known input injection. Collectors may collude with clients to
send known input to the aggregators, allowing collectors to
shrink the effective anonymity set by subtracting the known
inputs from the final output. Sybil attacks [Dou02] could be
used to amplify this capability.
7.1.4.2. Mitigations
1. Aggregators should refuse shared parameters that are trivially
insecure (i.e., aggregation threshold of 1 contribution).
2. If aggregator output satisfies differential privacy Section 7.5,
then genuine records are protected regardless of the size of the
anonymity set.
7.1.5. 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.
7.1.6. Attacker on the network
We assume the existence of attackers on the network links between
participants.
7.1.6.1. Capabilities
1. Observation of network traffic. Attackers may observe messages
exchanged between participants at the IP layer.
1. The time of transmission of input shares by clients could
reveal information about user activity.
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* 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.
2. Observation of message size could allow the attacker to learn
how much input is being submitted 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 aggregations the user has opted
into or out of.
2. Tampering with network traffic. Attackers may drop messages or
inject new messages into communications between participants.
7.1.6.2. Mitigations
1. All messages exchanged between participants in the system should
be encrypted.
2. All messages exchanged between aggregators, the collector and the
leader should be mutually authenticated so that network attackers
cannot impersonate participants.
3. Clients should be required to submit inputs at regular intervals
so that the timing of individual messages does not reveal
anything.
4. Clients should submit dummy inputs even for aggregations the user
has not opted into.
[[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
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. Client authentication or attestation
[TODO: Solve issue#89]
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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.thomson-http-oblivious] to forward inputs 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. Batch parameters
An important parameter of a DAP deployment is the minimum batch size.
If an aggregation includes too few inputs, then the outputs can
reveal information about individual participants. Aggregators use
the batch size field of the shared task parameters to enforce minimum
batch size during the collect protocol, but server 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.
The DAP parameters also specify the maximum number of times a report
can be used. Some protocols, such as Poplar [BBCGGI21], require
reports to be used in multiple batches spanning multiple collect
requests.
7.5. Differential privacy
Optionally, DAP deployments can choose to ensure their output F
achieves 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 outputs. 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
input in a batch except for one, that one record is still formally
protected.
[OPEN ISSUE: While parameters configuring the differential privacy
noise (like specific distributions / variance) can be agreed upon out
of band by the aggregators and collector, there may be benefits to
adding explicit protocol support by encoding them into task
parameters.]
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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 outputs 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 inputs.
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:
* HpkeConfig Section 4.3.1: "application/dap-hpke-config"
* Report Section 4.3.2: "application/dap-report"
* AggregateInitializeReq Section 4.5: "application/dap-aggregate-
initialize-req"
* AggregateInitializeResp Section 4.5: "application/dap-aggregate-
initialize-resp"
* AggregateContinueReq Section 4.5: "application/dap-aggregate-
continue-req"
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* AggregateContinueResp Section 4.5: "application/dap-aggregate-
continue-resp"
* AggregateShareReq Section 4.5: "application/dap-aggregate-share-
req"
* AggregateShareResp Section 4.5: "application/dap-aggregate-share-
resp"
* CollectReq Section 4.5: "application/dap-collect-req"
* CollectResp Section 4.5: "application/dap-collect-resp"
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" media type
Type name: application
Subtype name: dap-hpke-config
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.2
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
8.1.2. "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 4.3.2
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
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hors' Addresses section
Intended usage: COMMON
Restrictions on usage: N/A
Author: see Authors' Addresses section
Change controller: IESG
8.1.3. "application/dap-aggregate-initialize-req" media type
Type name: application
Subtype name: dap-aggregate-initialize-req
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.5
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
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Change controller: IESG
8.1.4. "application/dap-aggregate-initialize-resp" media type
Type name: application
Subtype name: dap-aggregate-initialize-resp
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.5
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
8.1.5. "application/dap-aggregate-continue-req" media type
Type name: application
Subtype name: dap-aggregate-continue-req
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Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.5
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
8.1.6. "application/dap-aggregate-continue-resp" media type
Type name: application
Subtype name: dap-aggregate-continue-resp
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.5
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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
8.1.7. "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 4.5
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
8.1.8. "application/dap-aggregate-share-resp" media type
Type name: application
Subtype name: dap-aggregate-share-resp
Required parameters: N/A
Optional parameters: None
Encoding considerations: only "8bit" or "binary" is permitted
Security considerations: see Section 4.5
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
8.1.9. "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 4.5
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
8.1.10. "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 4.5
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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8.2. 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]
8.3. 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]
8.4. 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>.
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[I-D.thomson-http-oblivious]
Thomson, M. and C. A. Wood, "Oblivious HTTP", Work in
Progress, Internet-Draft, draft-thomson-http-oblivious-02,
24 August 2021, <https://datatracker.ietf.org/doc/html/
draft-thomson-http-oblivious-02>.
[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>.
[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>.
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[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>.
[SHS] Dang, Q., "Secure Hash Standard", National Institute of
Standards and Technology report,
DOI 10.6028/nist.fips.180-4, July 2015,
<https://doi.org/10.6028/nist.fips.180-4>.
[VDAF] Barnes, R., Patton, C., and P. Schoppmann, "Verifiable
Distributed Aggregation Functions", Work in Progress,
Internet-Draft, draft-irtf-cfrg-vdaf-03, 24 August 2022,
<https://datatracker.ietf.org/doc/html/draft-irtf-cfrg-
vdaf-03>.
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>.
[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>.
Authors' Addresses
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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
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