{"id":"W2989265342","doi":"10.1109/access.2019.2953495","title":"Privacy-Preserving Collaborative Model Learning Scheme for E-Healthcare","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Key Research and Development Program of China; Xidian University; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Encryption; Computer security; Field (mathematics); Scheme (mathematics); Service (business); Health care; Information privacy","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":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005650665,0.0002620131,0.0003372096,0.000219854,0.0002747061,0.0006626878,0.05160835,0.0002253614,0.00001406932],"category_scores_gemma":[0.009298553,0.000261935,0.00007328198,0.001064519,0.00005445648,0.003486675,0.07081041,0.0005078646,0.00006620293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461297,"about_ca_system_score_gemma":0.0002965066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005190236,"about_ca_topic_score_gemma":0.00001696625,"domain_scores_codex":[0.9974851,0.00008495224,0.0003592126,0.0009551799,0.0004339443,0.0006815925],"domain_scores_gemma":[0.9908757,0.0003687292,0.0002741487,0.007916467,0.0004583439,0.00010658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001834761,0.0002788469,0.05437121,0.001532816,0.0002653989,0.00002995766,0.001966487,0.02851922,0.02624157,0.04441512,0.7999052,0.04229065],"study_design_scores_gemma":[0.0004384182,0.00007594316,0.0001652514,0.00007626526,0.00000332257,0.000001949305,0.00004484709,0.8391142,0.01189476,0.1446944,0.003189413,0.0003011977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1348103,0.0003028168,0.8307803,0.02915286,0.0008973047,0.001219924,0.00004462178,0.001556816,0.001235018],"genre_scores_gemma":[0.5701033,0.00005084641,0.4288219,0.0004670782,0.00006170976,0.0001635655,0.00001527358,0.00003352371,0.000282793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.810595,"threshold_uncertainty_score":0.9999833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05450144309094356,"score_gpt":0.3469036547501888,"score_spread":0.2924022116592452,"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."}}