{"id":"W4416961978","doi":"10.1109/pst65910.2025.11268888","title":"A Generic Framework for Privacy Risk Assessment of Machine Learning Models","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; York University; University of Guelph","funders":"National Research Council","keywords":"Information privacy; Testbed; Set (abstract data type); Risk assessment; Privacy by Design; Safeguard; Focus (optics); Privacy software","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002514257,0.0006108432,0.001025019,0.0005465607,0.001313685,0.0004898165,0.002585249,0.0004939595,0.0001429734],"category_scores_gemma":[0.002536236,0.000607818,0.000515857,0.001952799,0.00022727,0.0009316388,0.002632125,0.002263155,0.00000462251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520911,"about_ca_system_score_gemma":0.001161066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006392447,"about_ca_topic_score_gemma":0.00003526721,"domain_scores_codex":[0.9946889,0.0008706103,0.001338457,0.001379936,0.0008143528,0.0009077297],"domain_scores_gemma":[0.9940186,0.002411416,0.001251659,0.001523084,0.0006244588,0.0001707579],"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":[0.00002781978,0.0001256318,0.007334857,0.0001690976,0.0001760016,0.000001549607,0.000515598,0.5154149,0.00002159842,0.4202751,0.00004049412,0.05589741],"study_design_scores_gemma":[0.0009731086,0.0002828689,0.001341224,0.0002884885,0.0001863987,0.000001442166,0.00004773712,0.7802306,0.0001184971,0.2147962,0.001329211,0.0004041804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001638632,0.001006965,0.9841301,0.001685748,0.001842313,0.00114827,0.00001341418,0.000237465,0.00829704],"genre_scores_gemma":[0.4894069,0.0001782653,0.5085543,0.0001646328,0.0001000892,0.00004930331,0.00000500532,0.00002809911,0.00151337],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4877683,"threshold_uncertainty_score":0.9999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684883092377972,"score_gpt":0.3360739578851268,"score_spread":0.2992251269613471,"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."}}