{"id":"W4391912718","doi":"10.48550/arxiv.2402.09477","title":"PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Retraining; Audit; Computer science; Internet privacy; Computer security; Artificial intelligence; Business; Accounting","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0122515,0.0009712087,0.001745653,0.001042308,0.001346282,0.004176199,0.002852659,0.002066932,0.002763974],"category_scores_gemma":[0.05837929,0.0008910779,0.001368745,0.001290013,0.003165173,0.01007067,0.00831839,0.005541749,0.00106059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383175,"about_ca_system_score_gemma":0.003108338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009389047,"about_ca_topic_score_gemma":0.0008859758,"domain_scores_codex":[0.9792853,0.009763566,0.0009427744,0.003416811,0.005440981,0.001150499],"domain_scores_gemma":[0.9465294,0.0142644,0.00251813,0.03396301,0.00207652,0.0006486063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002257684,0.0007534728,0.01139858,0.0004900893,0.0005334282,0.0005406284,0.001224655,0.1963232,0.02459999,0.3015872,0.02425738,0.4360336],"study_design_scores_gemma":[0.00006590653,0.0002786065,0.0008148837,0.00006370383,0.00004825551,0.0003730055,0.00009536603,0.7396714,0.02296743,0.229048,0.006519296,0.00005410353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02505331,0.0004740582,0.9657715,0.00112504,0.0001199126,0.0001343772,0.000372735,0.004595861,0.00235324],"genre_scores_gemma":[0.8151018,0.0003049232,0.1795604,0.0008660926,0.0002132472,0.0002593772,0.0006797345,0.0005843093,0.002430121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0122515,"threshold_uncertainty_score":0.06479287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1222154769054377,"score_gpt":0.2227934190527114,"score_spread":0.1005779421472737,"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."}}