  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.
