{"id":"W4416273494","doi":"10.1016/j.eswa.2025.130167","title":"Preserving data and model privacy during inference and training","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; Research and Productivity Council; University of Victoria","funders":"National Research Council Canada; Alliance de recherche numérique du Canada; National Research Council","keywords":"Inference; Homomorphic encryption; Federated learning; Differential privacy; Information privacy; Table (database); Software deployment; Training (meteorology); Encryption","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0122713,0.001200781,0.002178529,0.001086493,0.001673636,0.006513002,0.003857291,0.002473455,0.002169643],"category_scores_gemma":[0.07351232,0.001458236,0.002471065,0.001448093,0.004015198,0.01131758,0.008011084,0.007038346,0.001005685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345522,"about_ca_system_score_gemma":0.00377715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150067,"about_ca_topic_score_gemma":0.001684819,"domain_scores_codex":[0.9862271,0.006402924,0.0009447966,0.00251526,0.002939068,0.00097088],"domain_scores_gemma":[0.9392683,0.02627604,0.001559246,0.03089271,0.001524963,0.0004787593],"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":[0.002089287,0.0004080629,0.009162197,0.0005616964,0.000594593,0.001104336,0.002194141,0.2392215,0.02070384,0.3892307,0.0085591,0.3261706],"study_design_scores_gemma":[0.00004679704,0.000100137,0.0006061116,0.00007119452,0.000104901,0.0006235425,0.0002210886,0.4925057,0.03356723,0.4674787,0.004636623,0.00003787056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01008688,0.0002009195,0.9871178,0.0009080162,0.00005151262,0.0000475638,0.0003013599,0.0004602195,0.000825755],"genre_scores_gemma":[0.6648569,0.0005287546,0.3282197,0.000820823,0.000229795,0.0002081198,0.001181093,0.0004169767,0.003537801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0122713,"threshold_uncertainty_score":0.0648976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05999986806079979,"score_gpt":0.321658714042964,"score_spread":0.2616588459821642,"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."}}