{"id":"W4317209824","doi":"10.1145/3580367","title":"Differentially Private Release of Heterogeneous Network for Managing Healthcare Data","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Flexibility (engineering); Computer science; Health care; Scalability; Variety (cybernetics); Data sharing; Data science; Enhanced Data Rates for GSM Evolution; Big data; Information exchange; Private information retrieval; Information sharing; Data mining; Computer security; World Wide Web; Database; Artificial intelligence","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":[0.003653309,0.0005139298,0.0008069944,0.0007425875,0.001253022,0.001532416,0.001999087,0.001124597,0.00139497],"category_scores_gemma":[0.009331152,0.0002969851,0.0007010311,0.001247158,0.001235641,0.005118817,0.003743903,0.001253355,0.0003669042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455601,"about_ca_system_score_gemma":0.001162384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009887405,"about_ca_topic_score_gemma":0.001088299,"domain_scores_codex":[0.996935,0.001279456,0.0001716311,0.0006484503,0.000713003,0.0002525245],"domain_scores_gemma":[0.9947119,0.001710701,0.0005585894,0.002522683,0.0003049737,0.0001912083],"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":[0.001572121,0.0002667675,0.005512164,0.0002587158,0.0001837162,0.001088419,0.001063185,0.4906022,0.02363507,0.193213,0.013932,0.2686726],"study_design_scores_gemma":[0.00004769675,0.0001061689,0.0005050456,0.00001306494,0.00003160691,0.0003578349,0.0001333829,0.9315695,0.008223752,0.0530332,0.005955028,0.00002376994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04900011,0.0003512402,0.9465158,0.000813713,0.00009285005,0.0002090414,0.0004293523,0.0006376378,0.001950362],"genre_scores_gemma":[0.8304092,0.0003183693,0.1646127,0.0003551002,0.00008484987,0.0001986053,0.0009307917,0.00007416061,0.003016237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003653309,"threshold_uncertainty_score":0.01932073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09979903213826967,"score_gpt":0.3332541246626596,"score_spread":0.23345509252439,"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."}}