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While indispensable for privacy, these same properties can also be misused to conceal illicit activity. This dual-use nature makes effective de\u2011anonymization essential under appropriate, policy-bounded oversight, so that harmful behavior can be uncovered without undermining legitimate use. Yet de\u2011anonymization 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\u2011anonymization, we model joint node\u2013technique selection as a tri-objective problem balancing attack gain (\n                    <jats:italic>AP<\/jats:italic>\n                    ), attack impact (\n                    <jats:italic>AI<\/jats:italic>\n                    ), and attack cost(\n                    <jats:italic>AC<\/jats:italic>\n                    ). For each feasible node\u2013method pair we compute these three metrics, extract the Pareto set, prune with\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\epsilon$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03f5<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$+50\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mo>+<\/mml:mo>\n                            <mml:mn>50<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    higher attack gain and roughly\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$-29\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mo>-<\/mml:mo>\n                            <mml:mn>29<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    lower attack Impact and attack cost compared with random selection.\n                  <\/jats:p>","DOI":"10.1186\/s42400-026-00590-7","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T06:48:22Z","timestamp":1782802102000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing multi-objective strategies for enhanced Tor De-anonymization"],"prefix":"10.1186","volume":"9","author":[{"given":"Yali","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruolin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangyi","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"issue":"2","key":"590_CR1","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s40092-018-0289-z","volume":"15","author":"L Abdullah","year":"2019","unstructured":"Abdullah L, Chan W, Afshari A (2019) Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. 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