Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense
Khashayar Khajavi, Tao Wang · 2026
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-timeseries 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 informationtheoretic guarantee.Comprehensive experiments on public realworld 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 percentage points compared with classic Tamaraw.