{"id":"W2802758511","doi":"10.1109/tcbb.2018.2833463","title":"SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol","year":2018,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Institute of General Medical Sciences; National Institute of Biomedical Imaging and Bioengineering; National Human Genome Research Institute","keywords":"Computer science; Homomorphic encryption; Cloud computing; Computer security; Cryptography; Leverage (statistics); Server; Encryption; Cryptographic protocol; Artificial intelligence; Computer network; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002892617,0.0002817085,0.0002359664,0.0004005142,0.0005706106,0.0001013152,0.004890578,0.0002514406,0.00001788861],"category_scores_gemma":[0.0004563689,0.000220958,0.00007835228,0.0004944979,0.0006619032,0.0005605002,0.0009331448,0.0004425052,0.00006090237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004062887,"about_ca_system_score_gemma":0.0001163739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004812542,"about_ca_topic_score_gemma":0.000004493521,"domain_scores_codex":[0.9984338,0.00006678878,0.0005003471,0.000403039,0.0002380725,0.0003579039],"domain_scores_gemma":[0.9970273,0.0003225059,0.0002181526,0.002112905,0.0002182671,0.0001008507],"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":[0.0006960371,0.001582641,0.0004662379,0.0008936958,0.0006484109,0.00003696636,0.001699256,0.06538201,0.001019072,0.0382976,0.07607943,0.8131986],"study_design_scores_gemma":[0.0003777844,0.000303742,0.00003213742,0.00004036295,0.000007301297,0.0000644724,0.000008025581,0.6246728,0.0005825097,0.3733907,0.0003475153,0.0001726786],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000815558,0.000008373117,0.9897169,0.003899816,0.0002878737,0.004467432,0.0001215097,0.0005530055,0.0001295669],"genre_scores_gemma":[0.1957508,0.0000116389,0.801496,0.0006617245,0.00003755904,0.001987473,0.00003586795,0.000009478272,0.000009452609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.813026,"threshold_uncertainty_score":0.9087992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03923440685827051,"score_gpt":0.3235101691213451,"score_spread":0.2842757622630746,"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."}}