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The traffic analysis models we use in the (TS-)SDA are only approximations of the real-world as well as the theoretical sending models. Therefore the distributions Rec(Si ) give us only partial information about the actual receivers and are sometimes (as we show) just wrong. before applying the information theoretic metrics for anonymity, one would need to look at the mutual information between Rec(Si ) and the actual receivers to understand the biases in the approximations. G. Danezis, C. Diaz, and C.

95) the TS-SDA performs even better. The higher number of replies introduces noise for the SDA, worsening its performance, while the TS-SDA can de-anonymize them more effectively. 6 we show the effects of changing the background traffic. The SDA is sensitive to other users having a non-uniform behaviour (all users send only to a limited set of k = 20 contacts – just as Alice does – instead of uniformly to all other N = 1000 users in the system), which worsens its accuracy. The TS-SDA, however, can handle this sort of traffic due to its estimation of the background noise.

The expectation value and the values one standard deviation away from it of such a timed dynamic pool mix are also shown on Figure 6. −1 which is greater than that The variance of the uniform distribution is (M+1) 12 of the binomial distribution, M/4. Note that given a set of values {0 . . M }, and a given expected value μ the maximum entropy distribution is of the following form: P (X = x) = Crx 2 Using the facts that the sum of the probabilities equals 1 and the expectation equals μ allows us to determine the values for constants C and r.

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