A Least Squares Approach to the Static Traffic Analysis of High-Latency Anonymous Communication Systems
This work addresses the vulnerability of anonymous communication systems to disclosure attacks, providing a more accurate analytical method for traffic analysis, though it is incremental in improving upon existing heuristic-based approaches.
The authors tackled the problem of de-anonymizing traffic in high-latency anonymous communication systems by proposing the Least Squares Disclosure Attack (LSDA), which estimates user profiles by solving a least squares problem and achieves greater accuracy than previous statistical attacks, as demonstrated empirically.
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.