USENIX Security2017Top-tier venue
Walkie-Talkie: An Efficient Defense Against Passive Website Fingerprinting Attacks
Tao Wang, Ian Goldberg
Abstract
Website fingerprinting (WF) is a traffic analysis attack that allows an eavesdropper to determine the web activity of a client, even if the client is using privacy technologies such as proxies, VPNs, or Tor. Recent work has highlighted the threat of website fingerprinting to privacy-sensitive web users. Many previously designed defenses against website fingerprinting have been broken by newer attacks that use better classifiers. The remaining effective defenses are inefficient: they hamper user experience and burden the server with large overheads. In this work we propose Walkie-Talkie, an effective and efficient WF defense. Walkie-Talkie modifies the browser to communicate in half-duplex mode rather than the usual full-duplex mode; half-duplex mode produces easily moldable burst sequences to leak less information to the adversary, at little additional overhead. Designed for the open-world scenario, Walkie-Talkie molds burst sequences so that sensitive and non-sensitive pages look the same. Experimentally, we show that Walkie-Talkie can defeat all known WF attacks with a bandwidth overhead of 31% and a time overhead of 34%, which is far more efficient than all effective WF defenses (often exceeding 100% for both types of overhead). In fact, we show that Walkie-Talkie cannot be defeated by any website fingerprinting attack, even hypothetical advanced attacks that use site link information, page visit rates, and intercell timing.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7d22c33d-16fb-466a-b361-4f10091a7df7Cited by top-tier papers36
- Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep LearningPayap Sirinam, Mohsen Imani, Marc Juarez, Matthew WrightCCS 2018 · 632 citations
- Automated Website Fingerprinting through Deep LearningVera Rimmer, Davy Preuveneers, Marc Juarez, Tom van Goethem et al.NDSS 2018 · 399 citations
- Robust Website Fingerprinting Through the Cache Occupancy ChannelAnatoly Shusterman, Lachlan Kang, Yarden Haskal, Yosef Meltser et al.USENIX Security 2019 · 159 citations
- Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial PerturbationsMilad Nasr, Alireza Bahramali, Amir HoumansadrUSENIX Security 2021 · 142 citations
- New Directions in Automated Traffic AnalysisJordan Holland, Paul Schmitt, Nick Feamster, Prateek MittalCCS 2021 · 122 citations
Builds on3
- Website Fingerprinting at Internet ScaleAndriy Panchenko, Fabian Lanze, Jan Pennekamp, Thomas Engel et al.NDSS 2016 · 625 citations
- k-fingerprinting: A Robust Scalable Website Fingerprinting TechniqueJamie Hayes, George DanezisUSENIX Security 2016 · 474 citations
- Beauty and the Burst: Remote Identification of Encrypted Video StreamsRoei Schuster, Vitaly Shmatikov, Eran TromerUSENIX Security 2017 · 205 citations
Related papers
- Surakav: Generating Realistic Traces for a Strong Website Fingerprinting DefenseJiajun Gong, Wuqi Zhang, Charles Zhang, Tao WangS&P 2022 · 64 citations
- Stop, Don't Click Here Anymore: Boosting Website Fingerprinting By Considering Sets of SubpagesAsya Mitseva, Andriy PanchenkoUSENIX Security 2024 · 18 citations
- High Precision Open-World Website FingerprintingTao WangS&P 2020 · 95 citations
- TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic SplittingWladimir De la Cadena, Asya Mitseva, Jens Hiller, Jan Pennekamp et al.CCS 2020 · 110 citations
- Measuring Information Leakage in Website Fingerprinting Attacks and DefensesShuai Li, Huajun Guo, Nicholas HopperCCS 2018 · 97 citations
