Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917326666858496 |
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| author | Khajavi, Khashayar Wang, Tao |
| author_facet | Khajavi, Khashayar Wang, Tao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense Khajavi, Khashayar Wang, Tao Cryptography and Security 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. |
| title | Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.01046 |