Optimizing multi-objective strategies for enhanced Tor De-anonymization

Abstract 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 $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about $$+50\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>50</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> higher attack gain and roughly $$-29\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>29</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> lower attack Impact and attack cost compared with random selection.