Understanding Statistical Disclosure: A Least Squares Approach

It is widely accepted that Disclosure Attacks are effective against high-latency anonymous communication systems. A number of Disclosure Attack variants can be found in the literature that effectively de-anonymize traffic sent through a threshold mix. Nevertheless, these attacks’ performance has been mostly evaluated through simulation and how their effectiveness varies with the parameters of the system is not well-understood. We present the LSDA, a novel disclosure attack based on the Maximum Likelihood (ML) approach, in which user profiles are estimated solving a Least Squares problem. Further, contrary to previous heuristic-based attacks, our approach allows to analytically derive formulae that characterize the profiling error of the LSDA with respect to the system’s parameters. We verify through simulation that our predictors for the error closely model reality, and that the LSDA recovers users’ profiles with greater accuracy than its predecessors.