Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses
نویسندگان
چکیده
منابع مشابه
Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses
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 formal guarantees on their security, which ...
متن کاملBayes , not Naïve : Security Bounds on Website
Website Fingerprinting (WF) attacks raise major concerns about users’ privacy. They employ Machine Learning (ML) techniques 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 formal guarantees on their secur...
متن کاملNew Approaches to Website Fingerprinting Defenses
Website fingerprinting attacks[10] enable an adversary to infer which website a victim is visiting, even if the victim uses an encrypting proxy, such as Tor[19]. Previous work has shown that all proposed defenses against website fingerprinting attacks are ineffective[5], [3]. This paper advances the study of website fingerprinting attacks and defenses in two ways. First, we develop bounds on th...
متن کاملComparing Website Fingerprinting Attacks and Defenses
Website fingerprinting attacks allow a local, passive eavesdropper to identify a web browsing client’s destination web page by extracting noticeable and unique features from her traffic. Such attacks magnify the gap between privacy and security — a client who encrypts her communication traffic may still have her browsing behaviour exposed to lowcost eavesdropping. Previous authors have shown th...
متن کاملWebsite Fingerprinting Defenses at the Application Layer
Website Fingerprinting (WF) allows a passive network adversary to learn the websites that a client visits by analyzing traffic patterns that are unique to each website. It has been recently shown that these attacks are particularly effective against .onion sites, anonymous web servers hosted within the Tor network. Given the sensitive nature of the content of these services, the implications of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings on Privacy Enhancing Technologies
سال: 2017
ISSN: 2299-0984
DOI: 10.1515/popets-2017-0046