CRDec 11, 2020

RegulaTor: A Straightforward Website Fingerprinting Defense

arXiv:2012.06609v349 citations
AI Analysis

This work addresses the practical deployment challenges of website fingerprinting defenses for privacy-enhancing technologies like Tor, offering a more efficient solution for users concerned about traffic analysis.

This paper introduces RegulaTor, a website fingerprinting defense that leverages common web browsing traffic patterns to reduce attack accuracy and overhead. In a closed-world setting, RegulaTor reduced the accuracy of the Tik-Tok attack from 66% to 25.4%, with 39.3% less bandwidth overhead than the leading moderate-overhead defense. In an open-world setting, it limited a precision-tuned Tik-Tok attack to an F-score of 0.135, compared to 0.625 for the best comparable defense.

Website Fingerprinting (WF) attacks are used by local passive attackers to determine the destination of encrypted internet traffic by comparing the sequences of packets sent to and received by the user to a previously recorded data set. As a result, WF attacks are of particular concern to privacy-enhancing technologies such as Tor. In response, a variety of WF defenses have been developed, though they tend to incur high bandwidth and latency overhead or require additional infrastructure, thus making them difficult to implement in practice. Some lighter-weight defenses have been presented as well; still, they attain only moderate effectiveness against recently published WF attacks. In this paper, we aim to present a realistic and novel defense, RegulaTor, which takes advantage of common patterns in web browsing traffic to reduce both defense overhead and the accuracy of current WF attacks. In the closed-world setting, RegulaTor reduces the accuracy of the state-of-the-art attack, Tik-Tok, against comparable defenses from 66% to 25.4%. To achieve this performance, it requires limited added latency and a bandwidth overhead 39.3% less than the leading moderate-overhead defense. In the open-world setting, RegulaTor limits a precision-tuned Tik-Tok attack to an F-score of .135, compared to .625 for the best comparable defense.

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