Abstract
While anonymization systems like mix networks can provide privacy to their users by, e.g., hiding their communication relationships, several traffic analysis attacks can deanonymize them. In this work, we examine Statistical Disclosure Attacks and introduce a new implementation called the Smart Noise Statistical Disclosure Attack. This attack can improve results by examining how often other users send together with the attacker’s target to better filter out the noise caused by them. We evaluate this attack by comparing it to previous variants in various simulations and thus show how it can improve upon them. Further, we demonstrate how other implementations can be improved by combing them with our approach to noise calculation. Finally, we critically review used evaluation metrics to determine their significance.
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Notes
- 1.
We allow senders and recipients to appear multiple times per round. Thus we talk about lists, not sets.
- 2.
This assumes random and independent senders. Thus the chance of a non-cloak background round appearing can be calculated (with values for round 25) as \((fraction\; of\; non-cloak\; users)^{b}=(1-\frac{cloak user count}{N})^{b}=(1-(\frac{1183}{20000}))^{50} \approx 0.047\). Note that there can be small but insignificant differences in the simulated results due to senders appearing multiple times per round, reducing the number of distinct cloak users.
- 3.
E.g., senders \(n_0\) and \(n_1\) are closer and send together more often than \(n_0\) and \(n_{50}\). Note that senders \(n_0\) and \(n_{99}\) are neighbors for \(N=100\).
- 4.
Or in the case of symmetrical communication, that her next communication (including messages received) will be performed with this user.
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Roßberger, M., Kesdoğan, D. (2024). Smart Noise Detection for Statistical Disclosure Attacks. In: Fritsch, L., Hassan, I., Paintsil, E. (eds) Secure IT Systems. NordSec 2023. Lecture Notes in Computer Science, vol 14324. Springer, Cham. https://doi.org/10.1007/978-3-031-47748-5_6
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