Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses

@article{Cherubin2017BayesNN,
  title={Bayes, not Na{\"i}ve: Security Bounds on Website Fingerprinting Defenses},
  author={Giovanni Cherubin},
  journal={PoPETs},
  year={2017},
  volume={2017},
  pages={215-231}
}
Website Fingerprinting (WF) attacks raise major concerns about users’ privacy. They employ Machine Learning (ML) to allow a local passive adversary to uncover the Web browsing behavior of a user, even if she browses through an encrypted tunnel (e.g. Tor, VPN). Numerous defenses have been proposed in the past; however, it is typically difficult to have… CONTINUE READING