USENIX Security2022Top-tier venue
QCSD: A QUIC Client-Side Website-Fingerprinting Defence Framework
Jean-Pierre Smith, Luca Dolfi, Prateek Mittal, Adrian Perrig
Abstract
Website fingerprinting attacks, which analyse the metadata of encrypted network communication to identify visited websites, have been shown to be effective on privacy-enhancing technologies including virtual private networks (VPNs) and encrypted proxies. Despite this, VPNs are still undefended against these attacks, leaving millions of users vulnerable. Proposed defences against website fingerprinting require cooperation between the client and a remote endpoint to reshape the network traffic, thereby hindering deployment. We observe that the rapid and wide-spread deployment of QUIC and HTTP/3 creates an exciting opportunity to build website-fingerprinting defences directly into client applications, such as browsers, without requiring any changes to web servers, VPNs, or the deployment of new network services. We therefore design and implement the QCSD framework, which leverages QUIC and HTTP/3 to emulate existing website-fingerprinting defences by bidirectionally adding cover traffic and reshaping connections solely from the client. As case studies, we emulate both the FRONT and Tamaraw defences solely from the client and collected several datasets of live-defended traffic on which we evaluated modern machine-learning based attacks. Our results demonstrate the promise of this approach in shaping connections towards client-orchestrated defences, thereby removing a primary barrier to the deployment of website-fingerprinting defences.
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Cited by top-tier papers4
- In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security ProblemsRoman Beltiukov, Wenbo Guo, Arpit Gupta, Walter WillingerCCS 2023 · 10 citations
- NetShaper: A Differentially Private Network Side-Channel Mitigation SystemAmir Sabzi, Rut Vora, Swati Goswami, Margo I. Seltzer et al.USENIX Security 2024 · 7 citations
- Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage FingerprintingBogdan Constantin Cebere, Prateek Kumar, Sylvain Chatel, Wouter Lueks et al.CCS 2026
- Endangered Privacy: Large-Scale Monitoring of Video Streaming ServicesMartin Björklund, Romaric DuvignauUSENIX Security 2025
Builds on10
- Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep LearningPayap Sirinam, Mohsen Imani, Marc Juarez, Matthew WrightCCS 2018 · 632 citations
- k-fingerprinting: A Robust Scalable Website Fingerprinting TechniqueJamie Hayes, George DanezisUSENIX Security 2016 · 474 citations
- Automated Website Fingerprinting through Deep LearningVera Rimmer, Davy Preuveneers, Marc Juarez, Tom van Goethem et al.NDSS 2018 · 399 citations
- Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot LearningPayap Sirinam, Nate Mathews, Mohammad Saidur Rahman, Matthew WrightCCS 2019 · 268 citations
- Walkie-Talkie: An Efficient Defense Against Passive Website Fingerprinting AttacksTao Wang, Ian GoldbergUSENIX Security 2017 · 249 citations
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