| Internet-Draft | Partitioning for Privacy | March 2023 |
| Kühlewind, et al. | Expires 14 September 2023 | [Page] |
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.¶
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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.¶
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:¶
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.¶
[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.¶
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.¶
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).¶
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.¶
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.¶
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.¶
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).¶
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:¶
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.¶
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.¶
There are multiple factors that lead to a diversity in approaches to partitioning, including:¶
The following section discusses currently on-going work in the IETF that is applying privacy partitioning.¶
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) 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:¶
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.¶
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.¶
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 Clients.¶
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.¶
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.¶
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.¶
Applying privacy partitioning to an existing or new system or protocol requires the following steps:¶
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 identity.¶
In this section, we discuss considerations for partitioning these types of 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 information.¶
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 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 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.¶
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:¶
We elaborate on each below.¶
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:¶
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.¶
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.¶
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.¶
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 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 future.¶
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.¶
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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