Understanding the Effects of Real-World Behavior in Statistical Disclosure Attacks

TL;DR

This paper evaluates the effectiveness of least squares disclosure attacks in real-world scenarios, revealing that previous models do not accurately predict privacy risks when applied to actual user behavior data.

Contribution

It extends prior analysis by relaxing assumptions and validating attack performance with real traffic data, providing new insights into privacy protection in practical settings.

Findings

Models from prior work do not fit real user behavior.

Relaxed assumptions improve understanding of attack effectiveness.

Real data validation shows different privacy risks than controlled scenarios.

Abstract

High-latency anonymous communication systems prevent passive eavesdroppers from inferring communicating partners with certainty. However, disclosure attacks allow an adversary to recover users' behavioral profiles when communications are persistent. Understanding how the system parameters affect the privacy of the users against such attacks is crucial. Earlier work in the area analyzes the performance of disclosure attacks in controlled scenarios, where a certain model about the users' behavior is assumed. In this paper, we analyze the profiling accuracy of one of the most efficient disclosure attack, the least squares disclosure attack, in realistic scenarios. We generate real traffic observations from datasets of different nature and find that the models considered in previous work do not fit this realistic behavior. We relax previous hypotheses on the behavior of the users and extend previous performance analyses, validating our results with real data and providing new insights into the parameters that affect the protection of the users in the real world.