wdiff rfc9614v1.txt rfc9614.txt

Internet Architecture Board (IAB) M. Kühlewind
Request for Comments: 9614 Ericsson Research
Category: Informational T. Pauly
ISSN: 2070-1721 Apple
C. A. Wood
Cloudflare
July 2024

Partitioning as an Architecture for Privacy

Abstract

This document describes the principle of privacy partitioning, which
selectively spreads data and communication across multiple parties as
a means to improve 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.

Status of This Memo

This document is not an Internet Standards Track specification; it is
published for informational purposes.

This document is a product of the Internet Architecture Board (IAB)
and represents information that the IAB has deemed valuable to
provide for permanent record. It represents the consensus of the
Internet Architecture Board (IAB). Documents approved for
publication by the IAB are not candidates for any level of Internet
Standard; see Section 2 of RFC 7841.

Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9614.

This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document.

Table of Contents

1. Introduction
2. Privacy Partitioning
2.1. Privacy Contexts
2.2. Context Separation
2.3. Approaches to Partitioning
3. A Survey of Protocols Using Partitioning
3.1. CONNECT Proxying and MASQUE
3.2. Oblivious HTTP and DNS
3.3. Privacy Pass
3.4. Privacy Preserving Measurement
4. Applying Privacy Partitioning
4.1. User-Identifying Information
4.2. Selecting Client Identifiers
4.3. Incorrect or Incomplete Partitioning
4.4. Selecting Information within a Context
5. Limits of Privacy Partitioning
5.1. Violations by Collusion
5.2. Violations by Insufficient or Incorrect Partitioning
5.2.1. Violations from Application Information
5.2.2. Violations from Network Information
5.2.3. Violations from Side Channels
5.2.4. Identifying Partitions
6. Partitioning Impacts
7. Security Considerations
8. IANA Considerations
9. Informative References
IAB Members at the Time of Approval
Acknowledgments
Authors' Addresses

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 are 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 remains private.

However, a secure channel between two endpoints is insufficient for
the 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 article; but the news service might
track which
specific articles they read on users access which articles, even if the user doesn't
want their account being recorded. activity to be tracked. This is problematic for
privacy since the service can link user activity to the user's
account.

* A client device that needs might need to upload metrics to an aggregation
service might want to be able to contribute data and in doing so allow the service to attribute the system
without having their
specific metrics contributions attributed to them. that client device. 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, in 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. Although privacy partitioning cannot guarantee there is no
link between user-specific identity and user-specific data, when
applied properly it helps ensure that user privacy violations become
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 Oblivious
HTTP Application Intermediation (OHAI), Multiplexed Application
Substrate over QUIC Encryption (MASQUE), Privacy Pass, and Privacy
Preserving Measurement (PPM). This document summarizes work in those
groups and describes a framework for reasoning thinking about the resulting
privacy posture of different endpoints in practice.

Privacy partitioning is particularly relevant as a tool for data
minimization, which is described in Section 6.1 of [RFC6973].
[RFC6973] provides guidance for privacy considerations in Internet
protocols, along with a set of questions on how to evaluate the data
minimization of a protocol in Section 7.1 of [RFC6973]. Protocols
that employ privacy partitioning ought to consider the questions in
that section when evaluating their design, particularly with regard
to how identifiers and data can be correlated by protocol
participants and observers in each context that has been partitioned.
Privacy partitioning can also be used as a way to separate identity
providers from relying parties (see Section 6.1.4 of [RFC6973]), as
in the case of Privacy Pass (see Section 3.3).

Privacy partitioning is not a panacea; applying it well requires
holistic analysis of the system in question to determine whether or
not partitioning as a tool, and as implemented, offers meaningful
privacy improvements. See Section 5 for more information.

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 any 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, metadata, and
the entities that share access to that information. In order to
prevent the 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:

| 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 the server. Middleboxes or
intermediaries might simply forward traffic or might terminate the
traffic at any layer (such as terminating the TCP connection from the
client and creating another TCP connection to the server).
Regardless of how the middlebox interacts with the traffic, for the
purposes of privacy partitioning, it is able to observe all of the
data in the context.

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.

+-------------------------------------------------------------------+
| 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
requests.

+-------------------------------------------------------------------+
| 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

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 Media Access
Control (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 the 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 identifiers 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:

* Cryptographic protection, such as the use of encryption to
specific parties, allows partitioning of contexts between
different parties (those with the ability to remove cryptographic
protections, and those without).

* Connection separation across time or space to allow partitioning
of contexts for different application transactions over the
network.

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

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. On the other hand,
Oblivious HTTP assumes servers that cooperate with context
separation and, thus, reduces the overall number of elements in
the solution.

* 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 whereby information only flows 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 whether 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 current on-going work in the IETF
that is applying privacy partitioning.

3.1. CONNECT Proxying and MASQUE

When using encryption on the connection between the client and the
proxy, HTTP forward proxies 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)
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 tunneled
between the client and target. This setup, as shown in Figure 4,
partitions communication into:

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

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

* 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; 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.

+-------------------------------------------------------------------+
| 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 neither.

In addition to the contexts described above for the single proxy
case, the two-hop proxy case shown in Figure 5 changes the contexts
in several ways:

* the Client-to-Target proxied context only includes the second
proxy (referred to here as "Proxy B");

* a new Client-to-Proxy B context is added, which is the TLS session
from the client to Proxy B that is also visible to the first proxy
(referred to here as "Proxy A");

* the contexts that see transport data only (TCP or UDP over IP) are
separated out into three separate contexts, a Client-to-Proxy A
context, a Proxy A-to-Proxy B context, and a Proxy B-to-Target
context.

+-------------------------------------------------------------------+
| Client-to-Target Encrypted Context |
| +--------+ +--------+ |
| | | | | |
| | Client +------------------HTTPS--------------------+ Target | |
| | | content | | |
| +--------+ +--------+ |
| |
+-------------------------------------------------------------------+
| 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 modes of proxying in 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.

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
an 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.

* End-to-end messages are encrypted between the Client and Gateway.
The content of these inner messages are visible to the Client,
Gateway, and Target. This is the Client-to-Target context.

* The encrypted messages exchanged between the Client and Gateway
are visible to the Relay, but the Relay cannot decrypt the
messages. This is the Client-to-Gateway context.

* The transport (such as TCP and TLS) connections between the
Client, Relay, and Gateway form two separate contexts: a Client-
to-Relay context and a Relay-to-Gateway context. It is 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 Clients.

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

Figure 6: Diagram of Oblivious HTTP Contexts

Oblivious DNS over HTTPS (ODoH) [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] [RFC9576] and a set of protocols
being developed in the Privacy Pass Working Group that allows clients
to present proof of verification in an anonymous and unlinkable
fashion via tokens. These tokens were originally designed as a way
to prove that a client had solved a CAPTCHA, but they 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 redeem 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 and 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

Since the redemption context and issuance context are separate
connections that involve separate entities, they can also be further
decoupled by running those parts of the protocols at different times.
Clients can fetch tokens through the issuance context early and cache
the tokens to for later use in redemption contexts. This can aid in
partitioning identifiers and data.

[PRIVACYPASS]

[RFC9576] describes different deployment models for which entities
operate origins, attesters, and issuers; in some models, they are all
separate entities, and in others they can be operated by the same
entity. The model impacts the effectiveness of partitioning, and
some models (such as when all three are operated by the same entity)
only provide effective partitioning when the timing of connections on
the two contexts are not correlated and when the client uses
different identifiers (such as different IP addresses) on each
context.

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. The 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 (in which the servers are separated into "Helper" and
"Leader" roles) 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 Partitioning

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 the sharing of
data across contexts wherever possible.

The most impactful types of information to partition are (a) user-
identifying information, such as user identifiers (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 a user
identifier.

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 and cause non-user-identifying information to
user-identifying. become
user-identifying information. For example, in CONNECT proxy systems
that use QUIC, the QUIC connection ID is inherently non-user-identifying non-user-
identifying since it is generated randomly (Section 5.1 of [QUIC]).
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 the network location and implementation of protocols in hardware
and software, and network
location. software. This information can be used to construct user-identifying 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 turn that
otherwise innocuous user data
to into user-identifying information.

4.2. Selecting Client Identifiers

The selection of client identifiers used in the contexts used for
privacy partitioning has a large impact on the effectiveness of
partitioning. Identifier selection can either undermine or improve
the value of partitioning. Generally, each context involves some
form of client identifier, which might be directly associated with a
client identity but can also be a pseudonym or a random one-time
identifier.

Using the same client identifier across multiple contexts can partly
or wholly undermine the effectiveness of partitioning by allowing the
various contexts to be linked back to the same client. For example,
if a client uses proxies as described in Section 3.1 to separate
connections but uses the same email address to authenticate to two
servers in different contexts, those actions can be linked back to
the same client. While this does not undermine all of the
partitioning achieved through proxying (the contexts along the
network path still cannot correlate the client identity and what
servers are being accessed), the overall effect of partitioning is
diminished.

When possible, using per-context unique client identifiers provides
better partitioning properties. For example, a client can use a
unique email address as an account identifier with each different
server it needs to log into. The same approach can apply across many
layers, as seen with per-network MAC address randomization
[RANDOM-MAC], use of multiple temporary IP addresses across
connections and over time [RFC8981], and use of unique per-
subscription identifiers for HTTP Web Push [RFC8030].

4.3. 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.

Evaluating and qualifying the resulting privacy of a system or
protocol that applies privacy partitioning depends on the contexts
that exist and the 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 that
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.

4.4. Selecting Information within a Context

Recognizing potential applications of privacy partitioning requires
identifying the contexts in use, the information exposed in a
context, and the intent of information exposed in a context.
Unfortunately, determining what information to include in a given
context is a non-trivial 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 client identifier, such as the client IP address,
coupled with client data to provide value. Partitioning contexts in
these systems such that they no longer determine 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, it is merely one of possibly many architectural tools that
help manage privacy risks. Understanding the limits of its benefits
requires a more comprehensive analysis of the system in question.
Such analysis also helps determine whether or not the tool has been
applied correctly. In particular, the value of privacy partitioning
depends on numerous factors, including, though not limited to, the
following:

* non-collusion across contexts and

* the type of information exposed in each context.

We elaborate on each in the following sections.

5.1. Violations by Collusion

Privacy partitions ensure that only the client, i.e., the entity that
is responsible for partitioning, can independently link all user-
specific information. 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. Thus, 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 affect the resulting impact on the user's
privacy.

As an example, consider Oblivious HTTP (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. Even if two entities
do not collude directly, if both entities reveal information to other
parties, it will not be possible to guarantee that the information
won't be combined. 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, along with
auditing to validate that the policies are being followed. For
cases where logging is required (such as for service operation),
such logged data should be minimized and anonymized to prevent it
from being useful for collusion.

* Protocol requirements to make collusion or data sharing more
difficult.

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

5.2. Violations by Insufficient or Incorrect Partitioning

Insufficient or incorrect application of privacy partitioning can
lessen or negate benefits to users. In particular, it is possible to
apply partitioning in a way that is either insufficient or incorrect
for meaningful privacy. For example, partitioning at one layer in
the stack can fail to account for linkable information at different
layers in the stack. Privacy violations can stem from partitioning
failures in a multitude of ways, some of which are described in the
following sections.

5.2.1. Violations from Application Information

Partitioning at the network layer can be insufficient when the
application layer fails to properly partition. 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 information, such as
the client's email address, location, or IP address. Even though
OHTTP separates the client IP address from the server via a relay,
the server can still learn this directly from the client's telemetry
report.

5.2.2. Violations from Network Information

It is also possible to inadequately partition at the network layer.
As an example, consider both TLS Encrypted Client Hello (ECH)
[TLS-ESNI] and VPNs. ECH uses cryptographic protection (encryption)
to hide information from unauthorized parties, but both clients and
servers (two entities) can link user-specific data to a user-specific
identifier (IP address). Similarly, while VPNs hide identifiers from
end servers, the VPN server can still see the identifiers 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.

5.2.3. Violations from Side Channels

Beyond the information that is intentionally revealed by applying
privacy partitioning, it is also possible for the 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.

5.2.4. Identifying Partitions

While straightforward violations of user privacy that stem from
insufficient partitioning may seem straightforward to mitigate, it
remains an open problem to rigorously determine what information
needs to be partitioned for meaningful privacy and to implement it in
a way that achieves the desired properties. In essence, it is
difficult to determine whether a certain set of information reveals
"too much" about a specific user, and it is similarly challenging to
determine whether or not an implementation of partitioning works as
intended. 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.

6. Partitioning Impacts

Applying privacy partitioning to communication protocols leads 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
usually passively observed in the network or metadata that has
been unintentionally revealed to the service provider can will no
longer be used for, available; for example, this can impact existing
security procedures such as application rate limiting or DDoS
mitigation. However,
presently deployed network-management Current network management techniques deployed often
rely on information that is exposed by most typical traffic but without any
guarantees that the information is accurate. lacks
guarantees or accuracy.

Privacy partitioning provides an opportunity for improvements in
these management techniques by enabling active exchange of
information with each entity in a privacy-preserving way and
requesting exactly the information needed for a specific task or
function rather than relying on the assumption that is information derived from a
limited set of unintentionally revealed information that cannot
be guaranteed to be present available and may disappear at any time be removed in the future.

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 reduce performance,
oftentimes the highly optimized nature of proxy-to-proxy paths
leads to improved performance.

Performance may also push back against the desire to

Reduced performance can be a reason that protocols and
deployments will not apply privacy partitioning. For example,
HTTPS connection reuse (Section 9.1.1 of [HTTP2]) allows clients
to use an existing HTTPS session created for one origin to
interact with different origins (provided that 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, but typically at the cost of less performance.

In general, while performance and privacy trade-offs 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 partitioned 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 in a public or otherwise compromised
location. Moreover, assume that the Target Resource for which
these requests are destined does not have such an implementation
flaw. Applications that 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 selected 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 trade-off encourages the centralization of
the Oblivious Relays.

7. Security Considerations

Section 5 discusses some of the limitations of privacy partitioning
in practice and advocates for holistic analysis to understand the
extent to which privacy partitioning offers meaningful privacy
improvements. Applied correctly, partitioning helps improve an end-
user's privacy posture, thereby making violations harder to do via
technical, social, or policy means. For example, side channels such
as traffic analysis [FINGERPINT] 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 that requires more effort, e.g.,
computational resources, to violate end-user privacy.

8. IANA Considerations

This document has no IANA actions.

9. Informative References

[CensusReconstruction]
United States Consensus Bureau, "The Census Bureau's
Simulated Reconstruction-Abetted Re-identification Attack
on the 2010 Census", May 2021,
<https://www.census.gov/data/academy/webinars/2021/
disclosure-avoidance-series/simulated-reconstruction-
abetted-re-identification-attack-on-the-2010-census.html>.

[CONNECT-IP]
Pauly, T., Ed., Schinazi, D., Chernyakhovsky, A.,
Kühlewind, M., and M. Westerlund, "Proxying IP in HTTP",
RFC 9484, DOI 10.17487/RFC9484, October 2023,
<https://www.rfc-editor.org/info/rfc9484>.

[CONNECT-UDP]
Schinazi, D. and L. Pardue, "HTTP Datagrams and the
Capsule Protocol", RFC 9297, DOI 10.17487/RFC9297, August
2022, <https://www.rfc-editor.org/info/rfc9297>.

[DataSetReconstruction]
Narayanan, A. and V. Shmatikov, "Robust De-anonymization
of Large Sparse Datasets", IEEE Symposium on Security and
Privacy, DOI 10.1109/sp.2008.33, May 2008,
<https://doi.org/10.1109/sp.2008.33>.

[DECOUPLING]
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,
<https://doi.org/10.1145/3563766.3564112>.

[DOH] Hoffman, P. and P. McManus, "DNS Queries over HTTPS
(DoH)", RFC 8484, DOI 10.17487/RFC8484, October 2018,
<https://www.rfc-editor.org/info/rfc8484>.

[FINGERPINT]
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, <https://datatracker.ietf.org/doc/html/draft-irtf-
pearg-website-fingerprinting-01>.

[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/info/rfc9180>.

[HTTP2] Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113,
DOI 10.17487/RFC9113, June 2022,
<https://www.rfc-editor.org/info/rfc9113>.

[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,
<https://www.rfc-editor.org/info/rfc9230>.

[OHTTP] Thomson, M. and C. A. Wood, "Oblivious HTTP", RFC 9458,
DOI 10.17487/RFC9458, January 2024,
<https://www.rfc-editor.org/info/rfc9458>.

[PRIVACYPASS]
Davidson, A., Iyengar, J., and C. A. Wood, "The Privacy
Pass Architecture", Work in Progress, Internet-Draft,
draft-ietf-privacypass-architecture-16, 25 September 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-
privacypass-architecture-16>.

[QUIC] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/info/rfc9000>.

[RANDOM-MAC]
Zuniga, JC., Bernardos, CJ., Ed., and A. Andersdotter,
"Randomized and Changing MAC Address state of affairs",
Work in Progress, Internet-Draft, draft-ietf-madinas-mac-
address-randomization-12, 28 February 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-madinas-
mac-address-randomization-12>.

[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,
<https://www.rfc-editor.org/info/rfc6973>.

[RFC8030] Thomson, M., Damaggio, E., and B. Raymor, Ed., "Generic
Event Delivery Using HTTP Push", RFC 8030,
DOI 10.17487/RFC8030, December 2016,
<https://www.rfc-editor.org/info/rfc8030>.

[RFC8981] Gont, F., Krishnan, S., Narten, T., and R. Draves,
"Temporary Address Extensions for Stateless Address
Autoconfiguration in IPv6", RFC 8981,
DOI 10.17487/RFC8981, February 2021,
<https://www.rfc-editor.org/info/rfc8981>.

[RFC9576] Davidson, A., Iyengar, J., and C. A. Wood, "The Privacy
Pass Architecture", RFC 9576, DOI 10.17487/RFC9576, June
2024, <https://www.rfc-editor.org/info/rfc9576>.

[TLS-ESNI] Rescorla, E., Oku, K., Sullivan, N., and C. A. Wood, "TLS
Encrypted Client Hello", Work in Progress, Internet-Draft,
draft-ietf-tls-esni-18, 4 March 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-tls-
esni-18>.

IAB Members at the Time of Approval

Internet Architecture Board members at the time this document was
approved for publication were:

Dhruv Dhody
Lars Eggert
Wes Hardaker
Cullen Jennings
Mallory Knodel
Suresh Krishnan
Mirja Kühlewind
Tommy Pauly
Alvaro Retana
David Schinazi
Christopher Wood
Qin Wu
Jiankang Yao

Acknowledgments

We would like to thank Martin Thomson, Eliot Lear, Mark Nottingham,
Niels ten Oever, Vittorio Bertola, Antoine Fressancourt, Cullen
Jennings, and Dhruv Dhody for their reviews and feedback.

Authors' Addresses

Mirja Kühlewind
Ericsson Research
Email: mirja.kuehlewind@ericsson.com

Tommy Pauly
Apple
Email: tpauly@apple.com

Christopher A. Wood
Cloudflare
Email: caw@heapingbits.net