Threading Statistical Disclosure Attack with EM: An Algorithm for Revealing Identity in Anonymous Communication Networks

Abstract

Messages sent across multiple platforms can be correlated to infer users’ attitudes, behaviors, preferences, lifestyles, and more. Therefore, research on anonymous communication systems has intensified in the last few years. This research introduces a new algorithm, Threading Statistical Disclosure Attack with EM (TSDA-EM), that employs real-world data to reveal communication’s behavior in an anonymous social network. In this study, we utilize a network constructed from email exchanges to represent interactions between individuals within an institution. The proposed algorithm is capable of identifying communication patterns within a mixed network, even under the observation of a global passive attacker. By employing multi-threading, this implementation reduced the average execution time by a factor of five when using a dataset with a large number of participants. Additionally, it has markedly improved classification accuracy, detecting more than 79% of users’ communications in large networks and more than 95% in small ones.