The bayesian traffic analysis of mix networks

摘要

This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network architectures is presented, that incorporates a number of attack techniques in the traffic analysis literature. We use the model to build an Markov Chain Monte Carlo inference engine, that calculates the probabilities of who is talking to whom given an observation of network traces. We provide a thorough evaluation of its correctness and performance, and confirm that mix networks with realistic parameters are secure. This approach enables us to apply established information theoretic anonymity metrics on complex mix networks, and extract information from anonymised traffic traces optimally.