{"id":"W4387928556","doi":"10.48550/arxiv.2310.13786","title":"Fundamental Limits of Membership Inference Attacks on Machine Learning Models","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut Universitaire de France; Agence Nationale de la Recherche","keywords":"Overfitting; Inference; Computer science; Machine learning; Statistical inference; Artificial intelligence; Point (geometry); Statistical model; Data mining; Mathematics; Artificial neural network; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005864261,0.0004926508,0.0005842962,0.0005533741,0.0002419581,0.0001134005,0.00277939,0.0003961234,0.00005168626],"category_scores_gemma":[0.0002762005,0.000582439,0.0002970599,0.0008817029,0.0001640793,0.0005455707,0.003551334,0.002073607,0.000135721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003016473,"about_ca_system_score_gemma":0.000219726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004521534,"about_ca_topic_score_gemma":0.00006768332,"domain_scores_codex":[0.9969373,0.000444331,0.0003650341,0.001446301,0.0002909875,0.0005160563],"domain_scores_gemma":[0.9970184,0.0007045091,0.0006271955,0.001290034,0.0001679012,0.000191947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004504316,0.00005667951,0.006525093,0.00009566429,0.00008420603,0.0001563205,0.0004582734,0.8304726,0.00001451274,0.1617092,0.00002572894,0.0003566978],"study_design_scores_gemma":[0.0004851654,0.0001368285,0.0006170677,0.0002914054,0.00004962342,0.000001819397,0.00009383677,0.9490402,0.00009291655,0.04864155,0.00005827899,0.0004912744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1451257,0.00003726209,0.847419,0.0001648464,0.0009506097,0.0003079091,0.00001854173,0.0007221494,0.005253977],"genre_scores_gemma":[0.9937851,0.0001313614,0.002917687,0.00005006498,0.00007449921,0.000001329513,0.00003862892,0.00005104064,0.002950282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8486595,"threshold_uncertainty_score":0.9996627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2513614997087211,"score_gpt":0.2536368033275691,"score_spread":0.002275303618848035,"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."}}