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Partitioning as an Architecture for Privacy

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Document Type
This is an older version of an Internet-Draft whose latest revision state is "Active".
Authors Mirja Kühlewind , Tommy Pauly , Christopher A. Wood
Last updated 2023-03-13
Replaces draft-kpw-iab-privacy-partitioning
RFC stream Internet Architecture Board (IAB)
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IAB shepherd (None)
Network Working Group                                       M. Kühlewind
Internet-Draft                                         Ericsson Research
Intended status: Informational                                  T. Pauly
Expires: 14 September 2023                                         Apple
                                                              C. A. Wood
                                                           13 March 2023

              Partitioning as an Architecture for Privacy


   This document describes the principle of privacy partitioning, which
   selectively spreads data and communication across multiple parties as
   a means to improve the privacy by separating user identity from user
   data.  This document describes emerging patterns in protocols to
   partition what data and metadata is revealed through protocol
   interactions, provides common terminology, and discusses how to
   analyze such models.

Discussion Venues

   This note is to be removed before publishing as an RFC.

   Discussion of this document takes place on the Internet Architecture
   Board Internet Engineering Task Force mailing list (,
   which is archived at .

   Source for this draft and an issue tracker can be found at

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   This Internet-Draft will expire on 14 September 2023.

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   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Privacy Partitioning  . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Privacy Contexts  . . . . . . . . . . . . . . . . . . . .   4
     2.2.  Context Separation  . . . . . . . . . . . . . . . . . . .   7
     2.3.  Approaches to Partitioning  . . . . . . . . . . . . . . .   7
   3.  A Survey of Protocols using Partitioning  . . . . . . . . . .   8
     3.1.  CONNECT Proxying and MASQUE . . . . . . . . . . . . . . .   8
     3.2.  Oblivious HTTP and DNS  . . . . . . . . . . . . . . . . .  12
     3.3.  Privacy Pass  . . . . . . . . . . . . . . . . . . . . . .  13
     3.4.  Privacy Preserving Measurement  . . . . . . . . . . . . .  14
   4.  Applying Privacy Partioning . . . . . . . . . . . . . . . . .  14
     4.1.  User-Identifying Information  . . . . . . . . . . . . . .  15
     4.2.  Incorrect or Incomplete Partitioning  . . . . . . . . . .  15
     4.3.  Identifying Information for Partitioning  . . . . . . . .  16
   5.  Limits of Privacy Partitioning  . . . . . . . . . . . . . . .  16
     5.1.  Violations by Collusion . . . . . . . . . . . . . . . . .  17
     5.2.  Violations by Insufficient Partitioning . . . . . . . . .  17
   6.  Partitioning Impacts  . . . . . . . . . . . . . . . . . . . .  18
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  20
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  20
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  20
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  22
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  22

1.  Introduction

   Protocols such as TLS and IPsec provide a secure (authenticated and
   encrypted) channel between two endpoints over which endpoints
   transfer information.  Encryption and authentication of data in
   transit is necessary to protect information from being seen or
   modified by parties other than the intended protocol participants.
   As such, this kind of security is necessary for ensuring that
   information transferred over these channels remain private.

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   However, a secure channel between two endpoints is insufficient for
   privacy of the endpoints themselves.  In recent years, privacy
   requirements have expanded beyond the need to protect data in transit
   between two endpoints.  Some examples of this expansion include:

   *  A user accessing a service on a website might not consent to
      reveal their location, but if that service is able to observe the
      client's IP address, it can learn something about the user's
      location.  This is problematic for privacy since the service can
      link user data to the user's location.

   *  A user might want to be able to access content for which they are
      authorized, such as a news article, without needing to have which
      specific articles they read on their account being recorded.  This
      is problematic for privacy since the service can link user
      activity to the user's account.

   *  A client device that needs to upload metrics to an aggregation
      service might want to be able to contribute data to the system
      without having their specific contributions being attribued to
      them.  This is problematic for privacy since the service can link
      client contributions to the specific client.

   The commonality in these examples is that clients want to interact
   with or use a service without exposing too much user-specific or
   identifying information to that service.  In particular, separating
   the user-specific identity information from user-specific data is
   necessary for privacy.  Thus, order to protect user privacy, it is
   important to keep identity (who) and data (what) separate.

   This document defines "privacy partitioning," sometimes also referred
   to as the "decoupling principle" [DECOUPLING], as the general
   technique used to separate the data and metadata visible to various
   parties in network communication, with the aim of improving user
   privacy.  Partitioning is a spectrum and not a panacea.  It is
   difficult to guarantee there is no link between user-specific
   identity and user-specific data.  However, applied properly, privacy
   partitioning helps ensure that user privacy violations becomes more
   technically difficult to achieve over time.

   Several IETF working groups are working on protocols or systems that
   adhere to the principle of privacy partitioning, including OHAI,
   MASQUE, Privacy Pass, and PPM.  This document summarizes work in
   those groups and describes a framework for reasoning about the
   resulting privacy posture of different endpoints in practice.

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   [RFC6973] discusses data minimization, especially in the context of
   user identity and identity management systems.  In these systems
   usually an identify provider issues credentials that can be used to
   access a service without revealing the user's identity by relying on
   the authentication assertion from the identity provider (see
   Section 6.1.4 of [RFC6973]).  This describes a specific form of
   privacy partitioning, similar as used for privacy pass (see
   Section Section 3.3).  Privacy partitioning as defined in this
   document goes further, to consider different deployment models that
   can create multiple contexts where data is minimized in each context.

2.  Privacy Partitioning

   For the purposes of user privacy, this document focuses on user-
   specific information.  This might include any identifying information
   that is specific to a user, such as their email address or IP
   address, or data about the user, such as their date of birth.
   Informally, the goal of privacy partitioning is to ensure that each
   party in a system beyond the user themselves only has access to one
   type of user-specific information.

   This is a simple application of the principle of least privilege,
   wherein every party in a system only has access to the minimum amount
   of information needed to fulfill their function.  Privacy
   partitioning advocates for this minimization by ensuring that
   protocols, applications, and systems only reveal user-specific
   information to parties that need access to the information for their
   intended purpose.

   Put simply, privacy partitioning aims to separate _who_ someone is
   from _what_ they do.  In the rest of this section, we describe how
   privacy partitioning can be used to achieve this goal.

2.1.  Privacy Contexts

   Each piece of user-specific information exists within some context,
   where a context is abstractly defined as a set of data and metadata
   and the entities that share access to that information.  In order to
   prevent correlation of user-specific information across contexts,
   partitions need to ensure that any single entity (other than the
   client itself) does not participate in more than one context where
   the information is visible.

   [RFC6973] discusses the importance of identifiers in reducing
   correlation as a way of improving privacy:

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   |  Correlation is the combination of various pieces of information
   |  related to an individual or that obtain that characteristic when
   |  combined... Correlation is closely related to identification.
   |  Internet protocols can facilitate correlation by allowing
   |  individuals' activities to be tracked and combined over time.
   |  Pseudonymity is strengthened when less personal data can be linked
   |  to the pseudonym; when the same pseudonym is used less often and
   |  across fewer contexts; and when independently chosen pseudonyms
   |  are more frequently used for new actions (making them, from an
   |  observer's or attacker's perspective, unlinkable).

   Context separation is foundational to privacy partitioning and
   reducing correlation.  As an example, consider an unencrypted HTTP
   session over TCP, wherein the context includes both the content of
   the transaction as well as any metadata from the transport and IP
   headers; and the participants include the client, routers, other
   network middleboxes, intermediaries, and server.

   | Context A                                                         |
   |  +--------+                +-----------+              +--------+  |
   |  |        +------HTTP------+           +--------------+        |  |
   |  | Client |                | Middlebox |              | Server |  |
   |  |        +------TCP-------+           +--------------+        |  |
   |  +--------+      flow      +-----------+              +--------+  |
   |                                                                   |

         Figure 1: Diagram of a basic unencrypted client-to-server
                        connection with middleboxes

   Adding TLS encryption to the HTTP session is a simple partitioning
   technique that splits the previous context into two separate
   contexts: the content of the transaction is now only visible to the
   client, TLS-terminating intermediaries, and server; while the
   metadata in transport and IP headers remain in the original context.
   In this scenario, without any further partitioning, the entities that
   participate in both contexts can allow the data in both contexts to
   be correlated.

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   | Context A                                                         |
   |  +--------+                                           +--------+  |
   |  |        |                                           |        |  |
   |  | Client +-------------------HTTPS-------------------+ Server |  |
   |  |        |                                           |        |  |
   |  +--------+                                           +--------+  |
   |                                                                   |
   | Context B                                                         |
   |  +--------+                +-----------+              +--------+  |
   |  |        |                |           |              |        |  |
   |  | Client +-------TCP------+ Middlebox +--------------+ Server |  |
   |  |        |       flow     |           |              |        |  |
   |  +--------+                +-----------+              +--------+  |
   |                                                                   |

       Figure 2: Diagram of how adding encryption splits the context
                                  into two

   Another way to create a partition is to simply use separate
   connections.  For example, to split two separate HTTP requests from
   one another, a client could issue the requests on separate TCP
   connections, each on a different network, and at different times; and
   avoid including obvious identifiers like HTTP cookies across the

   | Context A                                                         |
   |  +--------+                +-----------+              +--------+  |
   |  |        | IP A           |           |              |        |  |
   |  | Client +-------TCP------+ Middlebox +--------------+ Server |  |
   |  |        |      flow A    |     A     |              |        |  |
   |  +--------+                +-----------+              +--------+  |
   |                                                                   |
   | Context B                                                         |
   |  +--------+                +-----------+              +--------+  |
   |  |        | IP B           |           |              |        |  |
   |  | Client +-------TCP------+ Middlebox +--------------+ Server |  |
   |  |        |      flow B    |     B     |              |        |  |
   |  +--------+                +-----------+              +--------+  |
   |                                                                   |

        Figure 3: Diagram of making separate connections to generate
                             separate contexts

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   Using separate connections to create separate contexts can reduce or
   eliminate the ability of specific parties to correlate activity
   across contexts.  However, any identifier at any layer that is common
   across different contexts can be used as a way to correlate activity.
   Beyond IP addresses, many other factors can be used together to
   create a fingerprint of a specific device (such as MAC addresses,
   device properties, software properties and behavior, application
   state, etc).

2.2.  Context Separation

   In order to define and analyze how various partitioning techniques
   work, the boundaries of what is being partitioned need to be
   established.  This is the role of context separation.  In particular,
   in order to prevent correlation of user-specific information across
   contexts, partitions need to ensure that any single entity (other
   than the client itself) does not participate in contexts where both
   identities are visible.

   Context separation can be achieved in different ways, for example,
   over time, across network paths, based on (en)coding, etc.  The
   privacy-oriented protocols described in this document generally
   involve more complex partitioning, but the techniques to partition
   communication contexts still employ the same techniques:

   1.  Encryption allows partitioning of contexts within a given network

   2.  Using separate connections across time or space allow
       partitioning of contexts for different application transactions.

   These techniques are frequently used in conjunction for context
   separation.  For example, encrypting an HTTP exchange might prevent a
   network middlebox that sees a client IP address from seeing the user
   account identity, but it doesn't prevent the TLS-terminating server
   from observing both identities and correlating them.  As such,
   preventing correlation requires separating contexts, such as by using
   proxying to conceal a client IP address that would otherwise be used
   as an identifier.

2.3.  Approaches to Partitioning

   While all of the partitioning protocols described in this document
   create separate contexts using encryption and/or connection
   separation, each one has a unique approach that results in different
   sets of contexts.  Since many of these protocols are new, it is yet
   to be seen how each approach will be used at scale across the
   Internet, and what new models will emerge in the future.

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   There are multiple factors that lead to a diversity in approaches to
   partitioning, including:

   *  Adding privacy partitioning to existing protocol ecosystems places
      requirements and constraints on how contexts are constructed.
      CONNECT-style proxying is intended to work with servers that are
      unaware of privacy contexts, requiring more intermediaries to
      provide strong separation guarantees.  Oblivious HTTP, on the
      other hand, assumes servers that cooperate with context
      separation, and thus reduces the overall number of elements in the

   *  Whether or not information exchange needs to happen
      bidirectionally in an interactive fashion determines how contexts
      can be separated.  Some use cases, like metrics collection for
      PPM, can occur with information flowing only from clients to
      servers, and can function even when clients are no longer
      connected.  Privacy Pass is an example of a case that can be
      either interactive or not, depending on if tokens can be cached
      and reused.  CONNECT-style proxying and Oblivious HTTP often
      require bidirectional and interactive communication.

   *  The degree to which contexts need to be partitioned depends in
      part on the client's threat models and level of trust in various
      protocol participants.  For example, in Oblivious HTTP, clients
      allow relays to learn that clients are accessing a particular
      application-specific gateway.  If clients do not trust relays with
      this information, they can instead use a multi-hop CONNECT-style
      proxy approach wherein no single party learns whether specific
      clients are accessing a specific application.  This is the default
      trust model for systems like Tor, where multiple hops are used to
      drive down the probability of privacy violations due to collusion
      or related attacks.

3.  A Survey of Protocols using Partitioning

   The following section discusses currently on-going work in the IETF
   that is applying privacy partitioning.

3.1.  CONNECT Proxying and MASQUE

   HTTP forward proxies, when using encryption on the connection between
   the client and the proxy, provide privacy partitioning by separating
   a connection into multiple segments.  When connections to targets via
   the proxy themselves are encrypted, the proxy cannot see the end-to-
   end content.  HTTP has historically supported forward proxying for
   TCP-like streams via the CONNECT method.  More recently, the
   Multiplexed Application Substrate over QUIC Encryption (MASQUE)

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   working group has developed protocols to similarly proxy UDP
   [CONNECT-UDP] and IP packets [CONNECT-IP] based on tunneling.

   In a single-proxy setup there is a tunnel connection between the
   client and proxy and an end-to-end connection that is tunnelled
   between the client and target.  This setup, as shown in the figure
   below, partitions communication into:

   *  a Client-to-Proxy context, which contains the transport metadata
      between the client and the target, and the request to the proxy to
      open a connection to the target;

   *  a Client-to-Target proxied context, which is the end-to-end data
      to the target that is also visible to the proxy, such as a TLS

   *  a Client-to-Target encrypted context, which contains the end-to-
      end content with the TLS session to the target, such as HTTP

   *  and a Proxy-to-Target context, which for TCP and UDP proxying
      contains any packet header information that is added or modified
      by the proxy, e.g., the IP and TCP/UDP headers.

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   | Client-to-Target Encrypted Context                                |
   |  +--------+                                           +--------+  |
   |  |        |                                           |        |  |
   |  | Client +------------------HTTPS--------------------+ Target |  |
   |  |        |                 content                   |        |  |
   |  +--------+                                           +--------+  |
   |                                                                   |
   | Client-to-Target Proxied Context                                  |
   |  +--------+                +-----------+              +--------+  |
   |  |        |                |           |              |        |  |
   |  | Client +----Proxied-----+   Proxy   +--------------+ Target |  |
   |  |        |    TLS flow    |           |              |        |  |
   |  +--------+                +-----------+              +--------+  |
   |                                                                   |
   | Client-to-Proxy Context                                           |
   |  +--------+                +-----------+                          |
   |  |        |                |           |                          |
   |  | Client +---Transport----+   Proxy   |                          |
   |  |        |     flow       |           |                          |
   |  +--------+                +-----------+                          |
   |                                                                   |
   | Proxy-to-Target Context                                           |
   |                            +-----------+              +--------+  |
   |                            |           |              |        |  |
   |                            |   Proxy   +--Transport---+ Target |  |
   |                            |           |    flow      |        |  |
   |                            +-----------+              +--------+  |
   |                                                                   |

                Figure 4: Diagram of one-hop proxy contexts

   Using two (or more) proxies provides better privacy partitioning.  In
   particular, with two proxies, each proxy sees the Client metadata,
   but not the Target; the Target, but not the Client metadata; or

   | Client-to-Target Encrypted Context                                |
   |  +--------+                                           +--------+  |
   |  |        |                                           |        |  |
   |  | Client +------------------HTTPS--------------------+ Target |  |
   |  |        |                 content                   |        |  |
   |  +--------+                                           +--------+  |

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   |                                                                   |
   | Client-to-Target Proxied Context                                  |
   |  +--------+                           +-------+       +--------+  |
   |  |        |                           |       |       |        |  |
   |  | Client +----------Proxied----------+ Proxy +-------+ Target |  |
   |  |        |          TLS flow         |   B   |       |        |  |
   |  +--------+                           +-------+       +--------+  |
   |                                                                   |
   | Client-to-Proxy B Context                                         |
   |  +--------+         +-------+         +-------+                   |
   |  |        |         |       |         |       |                   |
   |  | Client +---------+ Proxy +---------+ Proxy |                   |
   |  |        |         |   A   |         |   B   |                   |
   |  +--------+         +-------+         +-------+                   |
   |                                                                   |
   | Client-to-Proxy A Context                                         |
   |  +--------+         +-------+                                     |
   |  |        |         |       |                                     |
   |  | Client +---------+ Proxy |                                     |
   |  |        |         |   A   |                                     |
   |  +--------+         +-------+                                     |
   |                                                                   |
   | Proxy A-to-Proxy B Context                                        |
   |                     +-------+         +-------+                   |
   |                     |       |         |       |                   |
   |                     | Proxy +---------+ Proxy |                   |
   |                     |   A   |         |   B   |                   |
   |                     +-------+         +-------+                   |
   |                                                                   |
   | Proxy B-to-Target Context                                         |
   |                                       +-------+       +--------+  |
   |                                       |       |       |        |  |
   |                                       | Proxy +-------+ Target |  |
   |                                       |   B   |       |        |  |
   |                                       +-------+       +--------+  |
   |                                                                   |

                Figure 5: Diagram of two-hop proxy contexts

   Forward proxying, such as the protocols developed in MASQUE, uses
   both encryption (via TLS) and separation of connections (via proxy
   hops that see only the next hop) to achieve privacy partitioning.

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3.2.  Oblivious HTTP and DNS

   Oblivious HTTP [OHTTP], developed in the Oblivious HTTP Application
   Intermediation (OHAI) working group, adds per-message encryption to
   HTTP exchanges through a relay system.  Clients send requests through
   an Oblivious Relay, which cannot read message contents, to an
   Oblivious Gateway, which can decrypt the messages but cannot
   communicate directly with the client or observe client metadata like
   IP address.  Oblivious HTTP relies on Hybrid Public Key Encryption
   [HPKE] to perform encryption.

   Oblivious HTTP uses both encryption and separation of connections to
   achieve privacy partitioning.  The end-to-end messages are encrypted
   between the Client and Gateway (forming a Client-to-Gateway context),
   and the connections are separated into a Client-to-Relay context and
   a Relay-to-Gateway context.  It is also important to note that the
   Relay-to-Gateway connection can be a single connection, even if the
   Relay has many separate Clients.  This provides better anonymity by
   making the pseudonym presented by the Relay to be shared across many

   | Client-to-Target Context                                          |
   |  +--------+                           +---------+     +--------+  |
   |  |        |                           |         |     |        |  |
   |  | Client +---------------------------+ Gateway +-----+ Target |  |
   |  |        |                           |         |     |        |  |
   |  +--------+                           +---------+     +--------+  |
   |                                                                   |
   | Client-to-Gateway Context                                         |
   |  +--------+         +-------+         +---------+                 |
   |  |        |         |       |         |         |                 |
   |  | Client +---------+ Relay +---------+ Gateway |                 |
   |  |        |         |       |         |         |                 |
   |  +--------+         +-------+         +---------+                 |
   |                                                                   |
   | Client-to-Relay Context                                           |
   |  +--------+         +-------+                                     |
   |  |        |         |       |                                     |
   |  | Client +---------+ Relay |                                     |
   |  |        |         |       |                                     |
   |  +--------+         +-------+                                     |
   |                                                                   |

                Figure 6: Diagram of Oblivious HTTP contexts

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   Oblivious DNS over HTTPS [ODOH] applies the same principle as
   Oblivious HTTP, but operates on DNS messages only.  As a precursor to
   the more generalized Oblivious HTTP, it relies on the same HPKE
   cryptographic primitives, and can be analyzed in the same way.

3.3.  Privacy Pass

   Privacy Pass is an architecture [PRIVACYPASS] and set of protocols
   being developed in the Privacy Pass working group that allow clients
   to present proof of verification in an anonymous and unlinkable
   fashion, via tokens.  These tokens originally were designed as a way
   to prove that a client had solved a CAPTCHA, but can be applied to
   other types of user or device attestation checks as well.  In Privacy
   Pass, clients interact with an attester and issuer for the purposes
   of issuing a token, and clients then interact with an origin server
   to redeeem said token.

   In Privacy Pass, privacy partitioning is achieved with cryptographic
   protection (in the form of blind signature protocols or similar) and
   separation of connections across two contexts: a "redemption context"
   between clients an origins (servers that request and receive tokens),
   and an "issuance context" between clients, attestation servers, and
   token issuance servers.  The cryptographic protection ensures that
   information revealed during the issuance context is separated from
   information revealed during the redemption context.

   | Redemption Context                                                |
   |  +--------+         +--------+                                    |
   |  |        |         |        |                                    |
   |  | Origin +---------+ Client |                                    |
   |  |        |         |        |                                    |
   |  +--------+         +--------+                                    |
   |                                                                   |
   | Issuance Context                                                  |
   |                     +--------+      +----------+      +--------+  |
   |                     |        |      |          |      |        |  |
   |                     | Client +------+ Attester +------+ Issuer |  |
   |                     |        |      |          |      |        |  |
   |                     +--------+      +----------+      +--------+  |
   |                                                                   |

               Figure 7: Diagram of contexts in Privacy Pass

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3.4.  Privacy Preserving Measurement

   The Privacy Preserving Measurement (PPM) working group is chartered
   to develop protocols and systems that help a data aggregation or
   collection server (or multiple, non-colluding servers) compute
   aggregate values without learning the value of any one client's
   individual measurement.  Distributed Aggregation Protocol (DAP) is
   the primary working item of the group.

   At a high level, DAP uses a combination of cryptographic protection
   (in the form of secret sharing amongst non-colluding servers) to
   establish two contexts: an "upload context" between clients and non-
   colluding aggregation servers wherein aggregation servers possibly
   learn client identity but nothing about their individual measurement
   reports, and a "collect context" wherein a collector learns aggregate
   measurement results and nothing about individual client data.

   | Upload Context                      | Collect Context    |
   |                     +------------+  |                    |
   |              +----->|   Helper   |  |                    |
   | +--------+   |      +------------+  |                    |
   | |        +---+             ^        |   +-----------+    |
   | | Client |                 |        |   | Collector |    |
   | |        +---+             v        |   +-----+-----+    |
   | +--------+   |      +------------+  |         |          |
   |              +----->|   Leader   |<-----------+          |
   |                     +------------+  |                    |

                    Figure 8: Diagram of contexts in DAP

4.  Applying Privacy Partioning

   Applying privacy partitioning to an existing or new system or
   protocol requires the following steps:

   1.  Identify the types of information used or exposed in a system or
       protocol, some of which can be used to identify a user or
       correlate to other contexts.

   2.  Partition data to minimize the amount of user-identifying or
       correlatable information in any given context to only include
       what is necessary for that context, and prevent sharing of data
       across contexts wherever possible.

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   The most impactful types of information to partition are (a) user-
   identifying information, such as user identity or identities
   (including account names or IP addresses) that can be linked and (b)
   non-user-identifying information (including content a user generates
   or accesses), which can be often sensitive when combined with user

   In this section, we discuss considerations for partitioning these
   types of information.

4.1.  User-Identifying Information

   User data can itself be user-identifying, in which case it should be
   treated as an identifier.  For example, Oblivious DoH and Oblivious
   HTTP partition the client IP address and client request data into
   separate contexts, thereby ensuring that no entity beyond the client
   can observe both.  Collusion across contexts could reverse this
   partitioning, but can also promote non-user-identifying information
   to user-identifying.  For example, in CONNECT proxy systems that use
   QUIC, the QUIC connection ID is inherently non-user-identifying since
   it is generated randomly ([QUIC], Section 5.1).  However, if combined
   with another context that has user-identifying information such as
   the client IP address, the QUIC connection ID can become user-
   identifying information.

   Some information is innate to client user-agents, including details
   of implementation of protocols in hardware and software, and network
   location.  This information can be used to construct user-identifying
   information, which is a process sometimes referred to as
   fingerprinting.  Depending on the application and system constraints,
   users may not be able to prevent fingerprinting in privacy contexts.
   As a result, fingerprinting information, when combined with non-user-
   identifying user data, could promote user data to user-identifying

4.2.  Incorrect or Incomplete Partitioning

   Privacy partitioning can be applied incorrectly or incompletely.
   Contexts may contain more user-identifying information than desired,
   or some information in a context may be more user-identifying than
   intended.  Moreover, splitting user-identifying information over
   multiple contexts has to be done with care, as creating more contexts
   can increase the number of entities that need to be trusted to not
   collude.  Nevertheless, partitions can help improve the client's
   privacy posture when applied carefully.

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   Evaluating and qualifying the resulting privacy of a system or
   protocol that applies privacy partitioning depends on the contexts
   that exist and types of user-identifying information in each context.
   Such evaluation is helpful for identifying ways in which systems or
   protocols can improve their privacy posture.  For example, consider
   DNS-over-HTTPS [DOH], which produces a single context which contains
   both the client IP address and client query.  One application of
   privacy partitioning results in ODoH, which produces two contexts,
   one with the client IP address and the other with the client query.

4.3.  Identifying Information for Partitioning

   Recognizing potential appliations of privacy partitoning requires
   identifying the contexts in use, the information exposed in a
   context, and the intent of information exposed in a context.
   Unfortunately, determing what information to include in a given
   context is a nontrivial task.  In principle, the information
   contained in a context should be fit for purpose.  As such, new
   systems or protocols developed should aim to ensure that all
   information exposed in a context serves as few purposes as possible.
   Designing with this principle from the start helps mitigate issues
   that arise if users of the system or protocol inadvertently ossify on
   the information available in contexts.  Legacy systems that have
   ossified on information available in contexts may be difficult to
   change in practice.  As an example, many existing anti-abuse systems
   depend on some notion of client identity such as client IP address,
   coupled with client data, to provide value.  Partitioning contexts in
   these systems such that they no longer see the client identity
   requires new solutions to the anti-abuse problem.

5.  Limits of Privacy Partitioning

   Privacy Partitioning aims to increase user privacy, though as stated
   is not a panacea.  The privacy properties depend on numerous factors,
   including, though not limited to:

   *  Non-collusion across contexts; and

   *  The type of information exposed in each context.

   We elaborate on each below.

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5.1.  Violations by Collusion

   Privacy partitions ensure that only the client, i.e., the entity
   which is responsible for partitioning, can link all user-specific
   information together up to collusion.  No other entity individually
   knows how to link all the user-specific information as long as they
   do not collude with each other across contexts.  This is why non-
   collusion is a fundamental requirement for privacy partitioning to
   offer meaningful privacy for end-users.  In particular, the trust
   relationships that users have with different parties affects the
   resulting impact on the user's privacy.

   As an example, consider OHTTP, wherein the Oblivious Relay knows the
   Client identity but not the Client data, and the Oblivious Gateway
   knows the Client data but not the Client identity.  If the Oblivious
   Relay and Gateway collude, they can link Client identity and data
   together for each request and response transaction by simply
   observing requests in transit.

   It is not currently possible to guarantee with technical protocol
   measures that two entities are not colluding.  However, there are
   some mitigations that can be applied to reduce the risk of collusion
   happening in practice:

   *  Policy and contractual agreements between entities involved in
      partitioning, to disallow logging or sharing of data, or to
      require auditing.

   *  Protocol requirements to make collusion or data sharing more

   *  Adding more partitions and contexts, to make it increasingly
      difficult to collude with enough parties to recover identities.

5.2.  Violations by Insufficient Partitioning

   It is possible to define contexts that contain more than one type of
   user-specific information, despite effort to do otherwise.  As an
   example, consider OHTTP used for the purposes of hiding client-
   identifying information for a browser telemetry system.  It is
   entirely possible for reports in such a telemetry system to contain
   both client-specific telemetry data, such as information about their
   specific browser instance, as well as client-identifying inforamtion,
   such as the client's location or IP address.  Even though OHTTP
   separates the client IP address from the server via a relay, the
   server still learns this directly from the client.

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   Other relevant examples of insufficient partitioning include TLS and
   Encrypted Client Hello (ECH) [I-D.ietf-tls-esni] and VPNs.  TLS and
   ECH use cryptographic protection (encryption) to hide information
   from unauthorized parties, but both clients and servers (two
   entities) can link user-specific data to user-specific identity (IP
   address).  Similarly, while VPNs hide identity from end servers, the
   VPN server has still can see the identity of both the client and
   server.  Applying privacy partitioning would advocate for at least
   two additional entities to avoid revealing both (identity (who) and
   user actions (what)) from each involved party.

   While straightforward violations of user privacy like this may seem
   straightforward to mitigate, it remains an open problem to determine
   whether a certain set of information reveals "too much" about a
   specific user.  There is ample evidence of data being assumed
   "private" or "anonymous" but, in hindsight, winds up revealing too
   much information such that it allows one to link back to individual
   clients; see [DataSetReconstruction] and [CensusReconstruction] for
   more examples of this in the real world.

   Beyond information that is intentionally revealed by applying privacy
   partitioning, it is also possible for information to be
   unintentionally revealed through side channels.  For example, in the
   two-hop proxy arrangement described in Section 3.1, Proxy A sees and
   proxies TLS data between the client and Proxy B.  While it does not
   directly learn information that Proxy B sees, it does learn
   information through metadata, such as the timing and size of
   encrypted data being proxied.  Traffic analysis could be exploited to
   learn more information from such metadata, including, in some cases,
   application data that Proxy A was never meant to see.  Although
   privacy partitioning does not obviate such attacks, it does increase
   the cost necessary to carry them out in practice.  See Section 7 for
   more discussion on this topic.

6.  Partitioning Impacts

   Applying privacy partitioning to communication protocols lead to a
   substantial change in communication patterns.  For example, instead
   of sending traffic directly to a service, essentially all user
   traffic is routed through a set of intermediaries, possibly adding
   more end-to-end round trips in the process (depending on the system
   and protocol).  This has a number of practical implications,
   described below.

   1.  Service operational or management challenges.  Information that
       is traditionally passively observed in the network or metadata
       that has been unintentionally revealed to the service provider
       cannot be used anymore for e.g., existing security procedures

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       such as application rate limiting or DDoS mitigation.  However,
       network management techniques deployed at present often rely on
       information that is exposed by most traffic but without any
       guarantees that the information is accurate.

       Privacy partitioning provides an opportunity for improvements in
       these management techniques with opportunities to actively
       exchange information with each entity in a privacy-preserving way
       and requesting exactly the information needed for a specific task
       or function rather then relying on assumption that are derived on
       a limited set of unintentionally revealed information which
       cannot be guaranteed to be present and may disappear any time in

   2.  Varying performance effects and costs.  Depending on how context
       separation is done, privacy partitioning may affect application
       performance.  As an example, Privacy Pass introduces an entire
       end-to-end round trip to issue a token before it can be redeemed,
       thereby decreasing performance.  In contrast, while systems like
       CONNECT proxying may seem like they would regress performance,
       often times the highly optimized nature of proxy-to-proxy paths
       leads to improved perforamnce.

       Performance may also push back against the desire to apply
       privacy partitioning.  For example, HTTPS connection reuse
       [HTTP2], Section 9.1.1 allows clients to use an existing HTTPS
       session created for one origin to interact with different origins
       (provided the original origin is authoritative for these
       alternative origins).  Reusing connections saves the cost of
       connection establishment, but means that the server can now link
       the client's activity with these two or more origins together.
       Applying privacy partitioning would prevent this, while typically
       at the cost of less performance.

       In general, while performance and privacy tradeoffs are often
       cast as a zero sum game, in practice this is often not the case.
       The relationship between privacy and performance varies depending
       on a number of related factors, such as application
       characteristics, network path properties, and so on.

   3.  Increased attack surface.  Even in the event that information is
       adequately partitioning across non-colluding parties, the
       resulting effects on the end-user may not always be positive.
       For example, using OHTTP as a basis for illustration, consider a
       hypothetical scenario where the Oblivious Gateway has an
       implementation flaw that causes all of its decrypt requests to be
       inappropriately logged to a public or otherwise compromised
       location.  Moreover, assume that the Target Resource for which

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       these requests are destined does not have such an implementation
       flaw.  Applications which use OHTTP with this flawed Oblivious
       Gateway to interact with the Target Resource risk their user
       request information being made public, albeit in a way that is
       decoupled from user identifying information, whereas applications
       that do not use OHTTP to interact with the Target Resource do not
       risk this type of disclosure.

   4.  Centralization.  Depending on the protocol and system, as well as
       the desired privacy properties, the use of partitioning may
       inherently force centralization to a select set of trusted
       participants.  As an example, the impact of OHTTP on end user
       privacy generally increases proportionally to the number of users
       that exist behind a given Oblivious Relay.  That is, the
       probability of an Oblivious Gateway determining the client
       associated with a request forwarded through an Oblivious Relay
       decreases as the number of possible clients behind the Oblivious
       Relay increases.  This tradeoff encourages centralization of the
       Oblivious Relays.

7.  Security Considerations

   Section 5 discusses some of the limitations of privacy partitioning
   in practice.  In general, privacy is best viewed as a spectrum and
   not a binary state (private or not).  Applied correctly, partitioning
   helps improve an end-users privacy posture, thereby making violations
   harder to do via technical, social, or policy means.  For example,
   side channels such as traffic analysis
   [I-D.irtf-pearg-website-fingerprinting] or timing analysis are still
   possible and can allow an unauthorized entity to learn information
   about a context they are not a participant of.  Proposed mitigations
   for these types of attacks, e.g., padding application traffic or
   generating fake traffic, can be very expensive and are therefore not
   typically applied in practice.  Nevertheless, privacy partitioning
   moves the threat vector from one that has direct access to user-
   specific information to one which requires more effort, e.g.,
   computational resources, to violate end-user privacy.

8.  IANA Considerations

   This document has no IANA actions.

9.  Informative References

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              "The Census Bureau's Simulated Reconstruction-Abetted Re-
              identification Attack on the 2010 Census", n.d.,

              Pauly, T., Schinazi, D., Chernyakhovsky, A., Kühlewind,
              M., and M. Westerlund, "Proxying IP in HTTP", Work in
              Progress, Internet-Draft, draft-ietf-masque-connect-ip-08,
              1 March 2023, <

              Schinazi, D. and L. Pardue, "HTTP Datagrams and the
              Capsule Protocol", RFC 9297, DOI 10.17487/RFC9297, August
              2022, <>.

              Narayanan, A. and V. Shmatikov, "Robust De-anonymization
              of Large Sparse Datasets", 2008 IEEE Symposium on Security
              and Privacy (sp 2008), DOI 10.1109/sp.2008.33, May 2008,

              Schmitt, P., Iyengar, J., Wood, C., and B. Raghavan, "The
              decoupling principle: a practical privacy framework",
              Proceedings of the 21st ACM Workshop on Hot Topics
              in Networks, DOI 10.1145/3563766.3564112, November 2022,

   [DOH]      Hoffman, P. and P. McManus, "DNS Queries over HTTPS
              (DoH)", RFC 8484, DOI 10.17487/RFC8484, October 2018,

   [HPKE]     Barnes, R., Bhargavan, K., Lipp, B., and C. Wood, "Hybrid
              Public Key Encryption", RFC 9180, DOI 10.17487/RFC9180,
              February 2022, <>.

   [HTTP2]    Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113,
              DOI 10.17487/RFC9113, June 2022,

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              Rescorla, E., Oku, K., Sullivan, N., and C. A. Wood, "TLS
              Encrypted Client Hello", Work in Progress, Internet-Draft,
              draft-ietf-tls-esni-15, 3 October 2022,

              Goldberg, I., Wang, T., and C. A. Wood, "Network-Based
              Website Fingerprinting", Work in Progress, Internet-Draft,
              draft-irtf-pearg-website-fingerprinting-01, 8 September
              2020, <

   [ODOH]     Kinnear, E., McManus, P., Pauly, T., Verma, T., and C.A.
              Wood, "Oblivious DNS over HTTPS", RFC 9230,
              DOI 10.17487/RFC9230, June 2022,

   [OHTTP]    Thomson, M. and C. A. Wood, "Oblivious HTTP", Work in
              Progress, Internet-Draft, draft-ietf-ohai-ohttp-07, 9
              March 2023, <

              Davidson, A., Iyengar, J., and C. A. Wood, "The Privacy
              Pass Architecture", Work in Progress, Internet-Draft,
              draft-ietf-privacypass-architecture-11, 6 March 2023,

   [QUIC]     Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
              Multiplexed and Secure Transport", RFC 9000,
              DOI 10.17487/RFC9000, May 2021,

   [RFC6973]  Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
              Morris, J., Hansen, M., and R. Smith, "Privacy
              Considerations for Internet Protocols", RFC 6973,
              DOI 10.17487/RFC6973, July 2013,


   TODO acknowledge.

Authors' Addresses

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   Mirja Kühlewind
   Ericsson Research

   Tommy Pauly

   Christopher A. Wood

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