{"id":"W4401508449","doi":"10.1109/infocom52122.2024.10621407","title":"Utility-Preserving Face Anonymization via Differentially Private Feature Operations","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; McMaster University","funders":"","keywords":"Computer science; Face (sociological concept); Feature (linguistics); Artificial intelligence; Data mining; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001768675,0.0000951025,0.00007886976,0.0002736322,0.0001714362,0.001235894,0.0007081853,0.00007789979,0.0003222086],"category_scores_gemma":[0.00005117998,0.00007932909,0.00005170398,0.001711133,0.00001943398,0.0008115633,0.0002918905,0.0001379229,0.0001834176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002077147,"about_ca_system_score_gemma":0.00005137577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004057545,"about_ca_topic_score_gemma":0.00008205615,"domain_scores_codex":[0.9989942,0.00006374704,0.0001559427,0.0003834023,0.0002510977,0.0001516032],"domain_scores_gemma":[0.9992687,0.00003806441,0.00001358201,0.0005269393,0.00008419189,0.00006854437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002536247,0.0001785725,0.0005475627,0.0001388511,0.00006770326,0.00001183112,0.002050405,0.0001960265,0.01842676,0.7887792,0.03762165,0.1519789],"study_design_scores_gemma":[0.00005858815,0.000007150707,0.00667102,0.00001012468,0.000004768355,0.000004667728,0.000009071196,0.9284565,0.003755275,0.001733972,0.05917154,0.0001172661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004810454,0.0003123888,0.9851364,0.005781897,0.0007891603,0.0001585518,0.000004719387,0.0005347545,0.002471609],"genre_scores_gemma":[0.950619,0.00003087433,0.03614392,0.0002265817,0.00005351829,0.000008295761,0.00004106795,0.000006659177,0.01287003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9489926,"threshold_uncertainty_score":0.9998009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898710890470407,"score_gpt":0.2631479453086716,"score_spread":0.2441608364039676,"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."}}