Internet DRAFT - draft-kpw-iab-privacy-partitioning
draft-kpw-iab-privacy-partitioning
Network Working Group M. Kühlewind
Internet-Draft Ericsson Research
Intended status: Informational T. Pauly
Expires: 24 April 2023 Apple
C. A. Wood
Cloudflare
21 October 2022
Partitioning as an Architecture for Privacy
draft-kpw-iab-privacy-partitioning-00
Abstract
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 (iab@iab.org),
which is archived at .
Source for this draft and an issue tracker can be found at
https://github.com/intarchboard/draft-obliviousness.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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material or to cite them other than as "work in progress."
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This Internet-Draft will expire on 24 April 2023.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Privacy Partitioning . . . . . . . . . . . . . . . . . . . . 4
2.1. Privacy Contexts . . . . . . . . . . . . . . . . . . . . 4
2.2. Context Separation . . . . . . . . . . . . . . . . . . . 6
3. A Survey of Protocols using Partitioning . . . . . . . . . . 7
3.1. CONNECT Proxying and MASQUE . . . . . . . . . . . . . . . 7
3.2. Oblivious HTTP and DNS . . . . . . . . . . . . . . . . . 10
3.3. Privacy Pass . . . . . . . . . . . . . . . . . . . . . . 11
3.4. Privacy Preserving Measurement . . . . . . . . . . . . . 12
4. Applying Privacy Partioning . . . . . . . . . . . . . . . . . 12
5. Limits of Privacy Partitioning . . . . . . . . . . . . . . . 14
5.1. Violations by Collusion . . . . . . . . . . . . . . . . . 14
5.2. Violations by Insufficient Partitioning . . . . . . . . . 15
6. Impacts of Partitioning . . . . . . . . . . . . . . . . . . . 15
7. Security Considerations . . . . . . . . . . . . . . . . . . . 16
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
9. Informative References . . . . . . . . . . . . . . . . . . . 17
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 18
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
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 inforamtion 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" 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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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:
"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)."
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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.
+-------------------------------------------------------------------+
| Context A |
| +--------+ +--------+ |
| | | | | |
| | Client |-------------------HTTPS-------------------| Server | |
| | | | | |
| +--------+ +--------+ |
| |
+-------------------------------------------------------------------+
| Context B |
| +--------+ +-----------+ +--------+ |
| | | | | | | |
| | Client |-------TCP------| Middlebox |--------------| Server | |
| | | flow | | | | |
| +--------+ +-----------+ +--------+ |
| |
+-------------------------------------------------------------------+
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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
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, e.g. 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:
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1. Encryption allows partitioning of contexts within a given network
path.
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.
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, provide privacy
partitioning by separating a connection into multiple segments. When
connections over 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 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 tunnelled
between the client and target. This setup, as shown in the figure
below, partitions communication into a Client-to-Proxy context (the
transport metadata between the client and the target, and the request
to the proxy to open a connection to the target), and a Client-to-
Target context (the end-to-end data, which generally would be a TLS-
encrypted connection). There is also a Proxy-to-Target context; in
case of MASQUE this context only contains any (unprotected) packet
header information that is added or modified by the proxy, e.g., the
IP and UDP headers.
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+-------------------------------------------------------------------+
| Client-to-Target Context |
| +--------+ +-----------+ +--------+ |
| | | | | | | |
| | Client |----Proxied-----| Proxy |--------------| Server | |
| | | flow | | | | |
| +--------+ +-----------+ +--------+ |
| |
+-------------------------------------------------------------------+
| Client-to-Proxy Context |
| +--------+ +-----------+ |
| | | | | |
| | Client |---Transport----| Proxy | |
| | | flow | | |
| +--------+ +-----------+ |
| |
+-------------------------------------------------------------------+
| Proxy-to-Target Context |
| +-----------+ +--------+ |
| | | | | |
| | Proxy |--Transport---| Server | |
| | | 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.
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+-------------------------------------------------------------------+
| Client-to-Target Context |
| +--------+ +-------+ +--------+ |
| | | | | | | |
| | Client |----------Proxied----------| Proxy |-------| Server | |
| | | 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 |-------| Server | |
| | 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 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
Clients.
+-------------------------------------------------------------------+
| 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 primatives, 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
identity or identities (such as an account name or IP address) that
can be linked and (b) user data (such as the content a user is
accessing), which can be often sensitive when combined with user
identity. Note that 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. Collusing across contexts
may reverses this partition process, 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.
This partitioning process 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 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.
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
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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.
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.
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 the requests in transit.
It is not currently possible to guarantee with technical protocol
measure 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
difficult.
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* 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.
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, and see Section 7 for more
discussion.
6. Impacts of Partitioning
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.
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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 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 by providing 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
future.
2. Varying performance effects. 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 perormance. 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. In general, while performance and
privacy tradeoffs are often cast as a zero sum game, in reality
this is often not the case.
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.
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8. IANA Considerations
This document has no IANA actions.
9. Informative References
[CensusReconstruction]
"The Census Bureau's Simulated Reconstruction-Abetted Re-
identification Attack on the 2010 Census", n.d.,
<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., Schinazi, D., Chernyakhovsky, A., Kühlewind,
M., and M. Westerlund, "IP Proxying Support for HTTP",
Work in Progress, Internet-Draft, draft-ietf-masque-
connect-ip-03, 27 September 2022,
<https://datatracker.ietf.org/doc/html/draft-ietf-masque-
connect-ip-03>.
[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/rfc/rfc9297>.
[DataSetReconstruction]
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,
<https://doi.org/10.1109/sp.2008.33>.
[DOH] Hoffman, P. and P. McManus, "DNS Queries over HTTPS
(DoH)", RFC 8484, DOI 10.17487/RFC8484, October 2018,
<https://www.rfc-editor.org/rfc/rfc8484>.
[HPKE] Barnes, R., Bhargavan, K., Lipp, B., and C. Wood, "Hybrid
Public Key Encryption", RFC 9180, DOI 10.17487/RFC9180,
February 2022, <https://www.rfc-editor.org/rfc/rfc9180>.
[I-D.ietf-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-15, 3 October 2022,
<https://datatracker.ietf.org/doc/html/draft-ietf-tls-
esni-15>.
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[I-D.irtf-pearg-website-fingerprinting]
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>.
[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/rfc/rfc9230>.
[OHTTP] Thomson, M. and C. A. Wood, "Oblivious HTTP", Work in
Progress, Internet-Draft, draft-ietf-ohai-ohttp-05, 26
September 2022, <https://datatracker.ietf.org/doc/html/
draft-ietf-ohai-ohttp-05>.
[PRIVACYPASS]
Davidson, A., Iyengar, J., and C. A. Wood, "The Privacy
Pass Architecture", Work in Progress, Internet-Draft,
draft-ietf-privacypass-architecture-08, 17 October 2022,
<https://datatracker.ietf.org/doc/html/draft-ietf-
privacypass-architecture-08>.
[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/rfc/rfc9000>.
[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/rfc/rfc6973>.
Acknowledgments
TODO acknowledge.
Authors' Addresses
Mirja Kühlewind
Ericsson Research
Email: mirja.kuehlewind@ericsson.com
Tommy Pauly
Apple
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Email: tpauly@apple.com
Christopher A. Wood
Cloudflare
Email: caw@heapingbits.net
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