{"id":"W4401110890","doi":"10.1109/intcec61833.2024.10602971","title":"A Comparative Study on Various ML Models Using Synthetic Data for Privacy Preservation","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Information privacy; Data modeling; Computer security; Internet privacy; Database","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":[0.01438562,0.001168707,0.0009691973,0.002610084,0.0007042254,0.002378284,0.001266052,0.001074353,0.0009955805],"category_scores_gemma":[0.05084392,0.0002417937,0.001106041,0.002542432,0.0009230791,0.002789125,0.001025158,0.001332941,0.0003021101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001725432,"about_ca_system_score_gemma":0.001136385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007077924,"about_ca_topic_score_gemma":0.004677441,"domain_scores_codex":[0.9932086,0.004509839,0.0004377166,0.0005642831,0.001033357,0.000246205],"domain_scores_gemma":[0.9193088,0.06609601,0.002638243,0.005357398,0.006207553,0.0003919853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009621928,0.0003326602,0.01854015,0.0007366785,0.0003116929,0.0001874158,0.0003975064,0.8492331,0.001339573,0.01201479,0.004448162,0.1114961],"study_design_scores_gemma":[0.00001793558,0.0002036102,0.002002734,0.00006405131,0.000039208,0.00009724973,0.0002372512,0.9906697,0.001661945,0.00373494,0.001245881,0.00002538065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6684034,0.01048023,0.3025864,0.003447271,0.0005187261,0.0004490082,0.003198535,0.001495092,0.00942135],"genre_scores_gemma":[0.9200065,0.001486977,0.07444856,0.0002118559,0.0001019468,0.0001270991,0.002858212,0.00008502827,0.0006738286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01438562,"threshold_uncertainty_score":0.07607931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2953082907629986,"score_gpt":0.3992457093920637,"score_spread":0.1039374186290651,"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."}}