The bayesian traffic analysis of mix networks

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 at-tack techniques in the traffic analysis literature. We use the model to build an Markov Chain Monte Carlo inference en-gine, 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 se-cure. This approach enables us to apply established informa-tion theoretic anonymity metrics on complex mix networks, and extract information from anonymised traffic traces op-timally