{"id":"W4413879034","doi":"10.1186/s12911-025-03109-1","title":"Enhancing privacy protection of physical examination data through synthetic algorithms based on differential privacy","year":2025,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Differential privacy; Health informatics; Computer science; Privacy protection; Information privacy; Algorithm; Internet privacy; Data mining; Public health; Medicine; Nursing","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":[0.01261456,0.0008655067,0.001195906,0.001159983,0.0008638432,0.002606782,0.001788997,0.001513149,0.001109377],"category_scores_gemma":[0.0450336,0.0004006327,0.001475286,0.001274965,0.001878123,0.003339423,0.00286859,0.001920217,0.0002987663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507415,"about_ca_system_score_gemma":0.002547056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001945215,"about_ca_topic_score_gemma":0.00133268,"domain_scores_codex":[0.9899742,0.005807795,0.000532113,0.001560951,0.001724771,0.0004001576],"domain_scores_gemma":[0.9686133,0.02166357,0.001975902,0.005345054,0.001982348,0.0004198087],"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":[0.0009769006,0.0003081547,0.02681384,0.0003619579,0.0003324262,0.0003165316,0.0006209599,0.644808,0.0078578,0.0790778,0.003313655,0.235212],"study_design_scores_gemma":[0.00005087471,0.0001295845,0.001677347,0.00002950162,0.00003881507,0.0002531032,0.00006599707,0.9541777,0.00428556,0.03755095,0.001714997,0.00002559376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08692703,0.0006709179,0.9087859,0.00103799,0.00006354719,0.0001337399,0.0003232229,0.0004370991,0.001620603],"genre_scores_gemma":[0.7933931,0.0004539165,0.2033156,0.0004458873,0.00009925663,0.000186206,0.00100807,0.00007120575,0.001026841],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01261456,"threshold_uncertainty_score":0.06671298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05910290888713675,"score_gpt":0.3450175718174333,"score_spread":0.2859146629302965,"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."}}