{"id":"W4392152646","doi":"10.1109/globecom54140.2023.10437155","title":"Efficient and Privacy-Preserving Logistic Regression Prediction over Vertically Partitioned Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Logistic regression; Computer science; Logistic model tree; Information privacy; Data mining; Data modeling; Machine learning; Database; Computer security","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":[0.002422734,0.0007318645,0.001076344,0.0004528767,0.0009030083,0.001197695,0.001691396,0.0006770336,0.00106315],"category_scores_gemma":[0.009855228,0.0004223385,0.0009573869,0.001158199,0.0009079622,0.003956364,0.003372451,0.00194953,0.000692313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009323149,"about_ca_system_score_gemma":0.001790003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002219567,"about_ca_topic_score_gemma":0.001535065,"domain_scores_codex":[0.9962655,0.001022486,0.0002375154,0.0007779382,0.001193457,0.0005031386],"domain_scores_gemma":[0.9932579,0.002107419,0.000869332,0.003021773,0.000569112,0.0001745973],"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":[0.002546461,0.0003179027,0.01785552,0.0002812165,0.0002744725,0.001312496,0.0008592915,0.5619399,0.06494287,0.06435518,0.008849031,0.2764656],"study_design_scores_gemma":[0.00003163105,0.00006883701,0.0006288862,0.00000790716,0.00001722508,0.0001615579,0.00006682896,0.9714383,0.01328875,0.01330228,0.0009665539,0.00002110626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08205394,0.000330708,0.9133725,0.0007398821,0.00003913759,0.0001060684,0.0004142619,0.001840965,0.001102587],"genre_scores_gemma":[0.9067277,0.0002382161,0.0906352,0.0001424642,0.000050815,0.00008767577,0.0006162237,0.00008334783,0.001418369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002422734,"threshold_uncertainty_score":0.01281279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09232312720548973,"score_gpt":0.3251130987540496,"score_spread":0.2327899715485598,"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."}}