{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005383591,0.0003248335,0.0004326373,0.0002562562,0.0003546084,0.0002648185,0.08199511,0.0001645016,0.00001209401],"category_scores_gemma":[0.002564168,0.0003263587,0.00009877373,0.001156016,0.0001294121,0.002623698,0.04028656,0.000354948,0.00007579925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006544717,"about_ca_system_score_gemma":0.0001637322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002299672,"about_ca_topic_score_gemma":0.000766352,"domain_scores_codex":[0.9965449,0.000136736,0.0005864924,0.001732139,0.0003340227,0.0006657352],"domain_scores_gemma":[0.9498137,0.001055512,0.0002005362,0.04875202,0.00007020216,0.0001080949],"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":[0.0002777313,0.0008178037,0.0004181078,0.0005959899,0.000854411,0.00005664916,0.0001907871,0.002014829,0.0007570342,0.002523889,0.2832617,0.7082311],"study_design_scores_gemma":[0.001049062,0.000204361,0.0007853986,0.0005074403,0.0001510667,0.00000676834,0.00004579326,0.6728387,0.0034879,0.3075603,0.01267839,0.0006847488],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007168433,0.0006564875,0.9493866,0.01007921,0.00158875,0.0005752866,0.02955222,0.0009772334,0.00001577128],"genre_scores_gemma":[0.5809961,0.001886667,0.3927747,0.0001807401,0.0002739936,0.0001243439,0.02353205,0.00009784979,0.0001335458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7075463,"threshold_uncertainty_score":0.9999188,"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."}}