{"id":"W2951244793","doi":"","title":"SecureMed: Secure Medical Computation using GPU-Accelerated Homomorphic Encryption Scheme.","year":2016,"lang":"en","type":"preprint","venue":"IACR Cryptology ePrint Archive","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Homomorphic encryption; Computer science; Speedup; Encryption; Scheme (mathematics); Cloud computing; Computation; Computer security; Parallel computing; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005415923,0.0003952327,0.0003954361,0.000427649,0.0003808981,0.0008216759,0.0008358458,0.0006825588,0.006526366],"category_scores_gemma":[0.001283813,0.0001838124,0.0003962218,0.0004204866,0.0005703758,0.001311783,0.001475316,0.0007566232,0.002038932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006294937,"about_ca_system_score_gemma":0.0008901771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007503647,"about_ca_topic_score_gemma":0.001344448,"domain_scores_codex":[0.9995059,0.0001218599,0.00002979524,0.00004135563,0.0002221486,0.00007901464],"domain_scores_gemma":[0.999598,0.00006973926,0.00003298909,0.0002056545,0.00005959332,0.00003407297],"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":[0.003985509,0.0005155805,0.004814317,0.000676618,0.0002773257,0.001582506,0.0005775384,0.1014623,0.1272611,0.2303041,0.08769564,0.4408474],"study_design_scores_gemma":[0.0004926939,0.0004072551,0.001462829,0.00005958493,0.00003701396,0.001144883,0.00007077026,0.7938013,0.08631935,0.05361553,0.06253073,0.00005801406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0915153,0.002001091,0.8557714,0.001628638,0.0004659197,0.0005056122,0.001364055,0.01249714,0.03425096],"genre_scores_gemma":[0.702566,0.0005103349,0.2779269,0.0004578097,0.00006961213,0.0002548205,0.001476435,0.0003171229,0.01642102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006526366,"threshold_uncertainty_score":0.02183294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0437951450094653,"score_gpt":0.3112288715510061,"score_spread":0.2674337265415408,"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."}}