Hang with your buddies to resist intersection attacks

By David Isaac Wolinsky, Ewa Syta, Bryan Ford. 2013

52 citations recorded by the source corpus.

Some anonymity schemes might in principle protect users from pervasive network surveillance--but only if all messages are independent and unlinkable. Users in practice often need pseudonymity--sending messages intentionally linkable to each other but not to the sender--but pseudonymity in dynamic networks exposes users to intersection attacks. We present Buddies, the first systematic design for intersection attack resistance in practical anonymity systems. Buddies groups users dynamically into buddy sets, controlling message transmission to make buddies within a set behaviorally indistinguishable under traffic analysis. To manage the inevitable tradeoffs between anonymity guarantees and communication responsiveness, Buddies enables users to select independent attack mitigation policies for each pseudonym. Using trace-based simulations and a working prototype, we find that Buddies can guarantee non-trivial anonymity set sizes in realistic chat/microblogging scenarios, for both short-lived and long-lived pseudonyms.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Henry Corrigan-Gibbs
2. Dan Boneh
3. Srini Devadas
4. David Lazar
5. Bryan Ford
6. David Mazières
7. Albert Kwon
8. Brent Waters

In Computer Networks and Communications

1. Srini Devadas
2. Dan Boneh
3. Henry Corrigan-Gibbs
4. Bryan Ford
5. David Lazar
6. David Mazières

In Information Systems

1. Henry Corrigan-Gibbs
2. Bryan Ford
3. Dan Boneh
4. David Lazar
5. Srini Devadas
6. Albert Kwon
7. Brent Waters
8. Xavier Boyen

Citations in the corpus

• Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “The second attack is an intersection attack [20, 49, 83, 122].”
• Riffle (2016, background): “Though Riffle does not protect against this class of attacks, it could benefit from prior work on mitigating these attacks [54].”
• Riposte: An Anonymous Messaging System Handling Millions of Users (2015, background): “These “intersection” o r “statistical disclosure” attacks affect many anonymity sy stems and defending against them is an important, albeit orthogon al, problem [57], [77].”