{"id":"W4386831200","doi":"10.1145/3620667","title":"Defenses to Membership Inference Attacks: A Survey","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Guangzhou University; National Natural Science Foundation of China","keywords":"Computer science; Inference; Intuition; Data science; Variety (cybernetics); Artificial intelligence; Key (lock); Machine learning; Computer security; Cognitive science","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":["metaresearch","metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.01771379,0.001078173,0.002651049,0.001041348,0.0003472265,0.0009367676,0.1214547,0.0007577246,0.00002177122],"category_scores_gemma":[0.4014489,0.001010061,0.000427029,0.006391015,0.0001507142,0.000392034,0.3422399,0.001433435,0.002968288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003272443,"about_ca_system_score_gemma":0.0007390438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009084312,"about_ca_topic_score_gemma":0.0007014857,"domain_scores_codex":[0.9870166,0.006427695,0.001531511,0.002523878,0.0009659459,0.001534441],"domain_scores_gemma":[0.9281972,0.03111707,0.001028615,0.03880179,0.0004617396,0.0003935995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[4.725267e-7,0.0000268536,0.0005762725,0.002386834,0.0001769225,0.00007370381,0.00003141442,0.000007579611,2.69259e-8,0.0001781912,0.08884621,0.9076955],"study_design_scores_gemma":[0.000404454,0.0003356345,0.009288104,0.04564955,0.000311863,0.0001775596,0.0000158589,0.01207007,0.000005582779,0.04337185,0.8822481,0.00612135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004628606,0.7780637,0.2093887,0.000884572,0.002744103,0.00127555,0.0003470738,0.007068214,0.0001818241],"genre_scores_gemma":[0.0001460144,0.8551186,0.1436915,0.000122643,0.000174459,0.00006085553,0.0004056438,0.0001722673,0.0001079986],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9015742,"threshold_uncertainty_score":0.999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3180780147503636,"score_gpt":0.4306536990287149,"score_spread":0.1125756842783514,"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."}}