Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense

• URL: http://arxiv.org/abs/2509.01046v1
• Date: Mon, 01 Sep 2025 01:07:02 GMT
• Title: Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense
• Abstract summary: Website fingerprinting (WF) attacks remain a significant threat to encrypted traffic.<n>We present a unified framework for designing an adaptive WF defense that combines the effectiveness of regularization with the provable security of supersequence-style grouping.<n>We instantiate the design as Adaptive Tamaraw, a variant of Tamaraw that assigns padding parameters on a per-cluster basis.
• Score: 2.4810953761205314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Website fingerprinting (WF) attacks remain a significant threat to encrypted traffic, prompting the development of a wide range of defenses. Among these, two prominent classes are regularization-based defenses, which shape traffic using fixed padding rules, and supersequence-based approaches, which conceal traces among predefined patterns. In this work, we present a unified framework for designing an adaptive WF defense that combines the effectiveness of regularization with the provable security of supersequence-style grouping. The scheme first extracts behavioural patterns from traces and clusters them into (k,l)-diverse anonymity sets; an early-time-series classifier (adapted from ECDIRE) then switches from a conservative global set of regularization parameters to the lighter, set-specific parameters. We instantiate the design as Adaptive Tamaraw, a variant of Tamaraw that assigns padding parameters on a per-cluster basis while retaining its original information-theoretic guarantee. Comprehensive experiments on public real-world datasets confirm the benefits. By tuning k, operators can trade privacy for efficiency: in its high-privacy mode Adaptive Tamaraw pushes the bound on any attacker's accuracy below 30%, whereas in efficiency-centred settings it cuts total overhead by 99% compared with classic Tamaraw.

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