{"id":"W2801958627","doi":"10.2196/medinform.8805","title":"Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute of Biomedical Imaging and Bioengineering; National Human Genome Research Institute; National Research Foundation of Korea; National Research Foundation","keywords":"Homomorphic encryption; Computer science; Encryption; Logistic regression; Machine learning; Cloud computing; Discriminative model; Support vector machine; Computation; Data mining; Artificial intelligence; Outsourcing; Classifier (UML); Algorithm; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003690063,0.000745469,0.0009143213,0.0006173601,0.0003214038,0.001260511,0.002199714,0.0009544839,0.0033338],"category_scores_gemma":[0.008720457,0.000372369,0.0004875373,0.0005829761,0.0007191679,0.002161703,0.001418484,0.001055962,0.001195311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204084,"about_ca_system_score_gemma":0.001299493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288918,"about_ca_topic_score_gemma":0.0006208787,"domain_scores_codex":[0.9970698,0.001109022,0.0001468405,0.0002964807,0.001133258,0.0002446282],"domain_scores_gemma":[0.9959037,0.001653158,0.0004394807,0.0008519134,0.0009442698,0.0002075144],"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":[0.003568576,0.001420534,0.007010076,0.0009230648,0.0003458593,0.0006904146,0.0001446015,0.5862992,0.02006319,0.01994442,0.006751638,0.3528385],"study_design_scores_gemma":[0.0001635482,0.0005643083,0.0003415363,0.00001328217,0.00002992487,0.0001949632,0.00001747004,0.9874207,0.008927232,0.001383894,0.0009284175,0.00001476955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1974242,0.002011867,0.7883007,0.001046921,0.0001374172,0.000958919,0.0002139346,0.00430473,0.005601278],"genre_scores_gemma":[0.8657269,0.0009397728,0.130449,0.0001491486,0.00003628685,0.0003240963,0.0002040047,0.0001383031,0.002032429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003690063,"threshold_uncertainty_score":0.01951516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05255794342517325,"score_gpt":0.3334524473528274,"score_spread":0.2808945039276542,"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."}}