Smart Noise Detection for Statistical Disclosure Attacks

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.