{"id":"W4313452912","doi":"10.1109/bibm55620.2022.9995700","title":"Private Federated Framework for Health Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Manitoba","funders":"","keywords":"Differential privacy; Computer science; Raw data; Architecture; Federated learning; Information privacy; Private information retrieval; Layer (electronics); Information sensitivity; Data modeling; Noise (video); Data mining; Computer security; Distributed computing; Artificial intelligence; Database","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":[0.007186667,0.000501707,0.0009961022,0.001162181,0.0009911909,0.003208267,0.003693109,0.001796981,0.005691177],"category_scores_gemma":[0.01070334,0.0004360442,0.001215133,0.001836216,0.001305252,0.006047551,0.005473112,0.002048901,0.001404117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002052123,"about_ca_system_score_gemma":0.003570475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002764238,"about_ca_topic_score_gemma":0.003295491,"domain_scores_codex":[0.9961306,0.001349477,0.000254028,0.0007867278,0.001146271,0.0003328907],"domain_scores_gemma":[0.9952769,0.00108064,0.0002394351,0.00247011,0.000653549,0.0002793618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007966458,0.0003697399,0.004291441,0.0003016408,0.0002084938,0.0005100275,0.0006067369,0.2285268,0.003512902,0.462662,0.02782044,0.2703932],"study_design_scores_gemma":[0.00004611716,0.00005998211,0.0003151773,0.00003674362,0.00003181403,0.000238013,0.00008035225,0.7883479,0.002977223,0.1866771,0.0211634,0.00002618386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004221027,0.0002398376,0.9901459,0.001011892,0.00005789983,0.0001088258,0.0004607867,0.002238774,0.001515018],"genre_scores_gemma":[0.4209864,0.0004480451,0.5670965,0.0009912314,0.0001562729,0.0004417524,0.00237436,0.000345234,0.007160243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007186667,"threshold_uncertainty_score":0.0380072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.130495123056648,"score_gpt":0.372480963103445,"score_spread":0.241985840046797,"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."}}