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Bayesian Inference and Traffic Analysis
If you have a question about this talk, please contact Andrew Lewis.
Traffic analysis attacks on anonymity networks were for long based on heuristics that allow an attacker to uncover communication partners under specific assumptions. However, slight changes in the model would render the methods useless. We present a general model for the analysis of mix networks which captures characteristics of anonymity systems subject to constraints while being able to accommodate most previously proposed attacks. Furthermore, we show how this model can be used to obtain the probabilities of who speaks with whom through the use of Bayesian Inference techniques and Markov Chain Monte Carlo simulations.
This talk is part of the Computer Laboratory Security Seminar series.
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Other listsBehavioural and Clinical Neuroscience Seminars Millennium Mathematics Project (http://maths.org) The Cambridge Group for the History of Population and Social Structure - seminar series
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