{"id":"W4406254925","doi":"10.1007/978-3-030-71522-9_1505","title":"Inverse Biometrics: Privacy, Risks, and Trust","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Internet privacy; Computer security; Computer science; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399225,0.000794627,0.0007702014,0.001105912,0.0009768654,0.007145742,0.0009855039,0.003060526,0.0145136],"category_scores_gemma":[0.00429333,0.0005463639,0.0004774047,0.00179466,0.005144131,0.009401729,0.002087306,0.003778645,0.005010315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588225,"about_ca_system_score_gemma":0.001097487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007466392,"about_ca_topic_score_gemma":0.0006401897,"domain_scores_codex":[0.9983481,0.0004680803,0.00006343464,0.0001816315,0.0008500691,0.00008867913],"domain_scores_gemma":[0.9985312,0.0008480915,0.000082607,0.0003509882,0.0001528964,0.00003418215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001377275,0.00001059381,0.00006378631,0.0001073055,0.00000445798,0.00005235427,0.0002794728,0.0005227159,0.0004185904,0.9116758,0.01714745,0.06970371],"study_design_scores_gemma":[0.000003971495,0.00002126251,0.0001456157,0.0002684976,0.000008864447,0.000834792,0.0002114286,0.002609929,0.001033216,0.723918,0.2709235,0.00002095564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005532411,0.1046591,0.2286316,0.01717659,0.002223827,0.00008548711,0.0002597178,0.0004534414,0.6409777],"genre_scores_gemma":[0.2655486,0.09459286,0.09520783,0.004859814,0.003117853,0.0002217582,0.0004166457,0.0004456799,0.535589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0145136,"threshold_uncertainty_score":0.04855287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06876341726728072,"score_gpt":0.2895737492346618,"score_spread":0.2208103319673811,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}