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<p>Tor employs multi-layer encryption and three-hop circuits to provide low-latency anonymity. While indispensable for privacy, these same properties can also be misused to conceal illicit activity. This dual-use nature makes effective de‑anonymization essential under appropriate, policy-bounded oversight, so that harmful behavior can be uncovered without undermining legitimate use. Yet de‑anonymization is not free: taking nodes offline and deploying honeypots consumes significant resources, increases exposure, and risks degrading network availability. Prior work faces two limitations: (i) it decouples the choice of which node to target from which method to apply, overlooking their strong coupling; and (ii) it often evaluates effectiveness with narrow, single-effect proxies, neglecting collateral network impact and operational cost. To support better de‑anonymization, we model joint node–technique selection as a tri-objective problem balancing attack gain (<i>AP</i>), attack impact (<i>AI</i>), and attack cost(<i>AC</i>). For each feasible node–method pair we compute these three metrics, extract the Pareto set, prune with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\epsilon\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϵ</mi> </math></EquationSource> </InlineEquation>-constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(+50\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>+</mo> <mn>50</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> higher attack gain and roughly <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(-29\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>-</mo> <mn>29</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> lower attack Impact and attack cost compared with random selection.</p>

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Optimizing multi-objective strategies for enhanced Tor De-anonymization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Yali Yuan,
  • Yuchen Zhang,
  • Ruolin Ma,
  • Liangyi Gong,
  • Guang Cheng

摘要

Tor employs multi-layer encryption and three-hop circuits to provide low-latency anonymity. While indispensable for privacy, these same properties can also be misused to conceal illicit activity. This dual-use nature makes effective de‑anonymization essential under appropriate, policy-bounded oversight, so that harmful behavior can be uncovered without undermining legitimate use. Yet de‑anonymization is not free: taking nodes offline and deploying honeypots consumes significant resources, increases exposure, and risks degrading network availability. Prior work faces two limitations: (i) it decouples the choice of which node to target from which method to apply, overlooking their strong coupling; and (ii) it often evaluates effectiveness with narrow, single-effect proxies, neglecting collateral network impact and operational cost. To support better de‑anonymization, we model joint node–technique selection as a tri-objective problem balancing attack gain (AP), attack impact (AI), and attack cost(AC). For each feasible node–method pair we compute these three metrics, extract the Pareto set, prune with \(\epsilon\) ϵ -constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about \(+50\%\) + 50 % higher attack gain and roughly \(-29\%\) - 29 % lower attack Impact and attack cost compared with random selection.