{"id":"W2994849590","doi":"10.1109/cwit.2019.8929901","title":"Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms","year":2019,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Information leakage; Computer science; Information privacy; Adversary; Bernoulli's principle; Mutual information; MNIST database; Gaussian; Metric (unit); Information sensitivity; Confidentiality; Bernoulli distribution; Data mining; Computer security; Random variable; Artificial intelligence; Mathematics; Deep learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02188885,0.001511483,0.002801959,0.002084804,0.001945508,0.005328964,0.004953132,0.003327189,0.001703215],"category_scores_gemma":[0.05685909,0.001048564,0.002383694,0.003659782,0.005001696,0.0120867,0.01047109,0.004493181,0.0005780986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002717921,"about_ca_system_score_gemma":0.002975956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002870283,"about_ca_topic_score_gemma":0.0002435197,"domain_scores_codex":[0.9664279,0.01678861,0.002230545,0.004231935,0.008710613,0.00161043],"domain_scores_gemma":[0.942275,0.02819919,0.006611535,0.01931535,0.002659776,0.0009391345],"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":[0.0008893339,0.0004887149,0.003565972,0.0005600964,0.0004502568,0.0004107313,0.001092523,0.2432123,0.01674356,0.5668434,0.004582688,0.1611604],"study_design_scores_gemma":[0.0001137545,0.0003299794,0.0007285969,0.0001061317,0.0001330463,0.0008989247,0.0001839198,0.5109293,0.03151986,0.4497167,0.005218033,0.0001217891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01651948,0.0005181847,0.9796163,0.0009268453,0.00003262898,0.0001133191,0.0001879338,0.0004130147,0.001672419],"genre_scores_gemma":[0.7798048,0.00047174,0.2165208,0.0005864883,0.0001497378,0.0003821945,0.0002512199,0.00009714419,0.001735841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02188885,"threshold_uncertainty_score":0.1157607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03231458839487675,"score_gpt":0.2598067093014645,"score_spread":0.2274921209065877,"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."}}