  Website fingerprinting (WF) attacks, usually conducted with the help of a
machine learning-based classifier, enable a network eavesdropper to pinpoint
which web page a user is accessing through the inspection of traffic patterns.
These attacks have been shown to succeed even when users browse the Internet
through encrypted tunnels, e.g., through Tor or VPNs. To assess the security of
new defenses against WF attacks, recent works have proposed feature-dependent
theoretical frameworks that estimate the Bayes error of an adversary's features
set or the mutual information leaked by manually-crafted features.
Unfortunately, as state-of-the-art WF attacks increasingly rely on deep
learning and latent feature spaces, security estimations based on simpler (and
less informative) manually-crafted features can no longer be trusted to assess
the potential success of a WF adversary in defeating such defenses. In this
work, we propose DeepSE-WF, a novel WF security estimation framework that
leverages specialized kNN-based estimators to produce Bayes error and mutual
information estimates from learned latent feature spaces, thus bridging the gap
between current WF attacks and security estimation methods. Our evaluation
reveals that DeepSE-WF produces tighter security estimates than previous
frameworks, reducing the required computational resources to output security
estimations by one order of magnitude.
