A Least Squares Approach to the Static Traffic Analysis of High-Latency Anonymous Communication Systems

TL;DR

This paper introduces LSDA, a least squares-based method for analyzing and de-anonymizing high-latency anonymous communication systems, providing analytical insights and improved accuracy over previous heuristic attacks.

Contribution

The paper presents LSDA, a novel least squares approach for traffic analysis that extends to pool mixes and offers analytical profiling error expressions.

Findings

LSDA outperforms previous heuristic attacks in profile recovery accuracy.

Analytical expressions accurately predict LSDA's performance.

LSDA is applicable to both threshold and pool mixes.

Abstract

Mixes, relaying routers that hide the relation between incoming and outgoing messages, are the main building block of high-latency anonymous communication networks. A number of so-called disclosure attacks have been proposed to effectively de-anonymize traffic sent through these channels. Yet, the dependence of their success on the system parameters is not well-understood. We propose the Least Squares Disclosure Attack (LSDA), in which user profiles are estimated by solving a least squares problem. We show that LSDA is not only suitable for the analysis of threshold mixes, but can be easily extended to attack pool mixes. Furthermore, contrary to previous heuristic-based attacks, our approach allows us to analytically derive expressions that characterize the profiling error of LSDA with respect to the system parameters. We empirically demonstrate that LSDA recovers users' profiles with greater accuracy than its statistical predecessors and verify that our analysis closely predicts actual performance.