Perfect Matching Disclosure Attacks

Traffic analysis is the best known approach to uncover relationships amongst users of anonymous communication systems, such as mix networks. Surprisingly, all previously published techniques require very specific user behavior to break the anonymity provided by mixes. At the same time, it is also well known that none of the considered user models reflects realistic behavior which casts some doubt on previous work with respect to real-life scenarios. We first present a user behavior model that, to the best of our knowledge, is the least restrictive scheme considered so far. Second, we develop the Perfect Matching Disclosure Attack, an efficient attack based on graph theory that operates without any assumption on user behavior. The attack is highly effective when de-anonymizing mixing rounds because it considers all users in a round at once, rather than single users iteratively. Furthermore, the extracted sender-receiver relationships can be used to enhance user profile estimations. We extensively study the effectiveness and efficiency of our attack and previous work when de-anonymizing users communicating through a threshold mix. Empirical results show the advantage of our proposal. We also show how the attack can be refined and adapted to different scenarios including pool mixes, and how precision can be traded in for speed, which might be desirable in certain cases.