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Abstract-The Statistical Disclosure Attack against a particular user of an anonymity system is known to be very effective in determining, after long-term observation of the system, the set of receivers that user sends messages to. This paper first presents an improvement over this attack that, by employing a weighted mean of the observed relative receiver popularity, is more accurate than the original one based upon arithmetic mean. Second, a mathematical analysis is presented of this attack on a model, in which senders blend dummy messages with real ones. It is shown that despite such sender-generated dummy cover traffic, the attack can proceed almost unhindered. The analysis substantiates earlier empirical indications of the ineffectiveness of this countermeasure.

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Abstract.We give a critical analysis of the system-wide anonymity metric of Edman et al. [3], which is based on the permanent value of a doubly-stochastic matrix. By providing an intuitive understanding of the permanent of such a matrix, we show that a metric that looks no further than this composite value is at best a rough indicator of anonymity. We identify situations where its inaccuracy is acute, and reveal a better anonymity indicator. Also, by constructing an information-preserving embedding of a smaller class of attacks into the wider class for which this metric was proposed, we show that this metric fails to possess desirable generalization properties. Finally, we present a new anonymity metric that does not exhibit these shortcomings. Our new metric is accurate as well as general.

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