{"id":"W4412708891","doi":"10.1016/j.patter.2025.101320","title":"A consensus privacy metrics framework for synthetic data","year":2025,"lang":"en","type":"article","venue":"Patterns","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Agricultural Research Institute of Ontario; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Berlin Institute of Health; Innovative Health Initiative; Institució Catalana de Recerca i Estudis Avançats; Instituto Nacional de Ciberseguridad; Generalitat de Catalunya; National Institutes of Health; Canada Research Chairs; Deutsche Forschungsgemeinschaft; UK Research and Innovation; U.S. National Library of Medicine; Alberta Machine Intelligence Institute; Canadian Institute for Advanced Research; European Commission; U.S. Census Bureau; Bill and Melinda Gates Foundation","keywords":"Computer science; Data science; Internet privacy; Data mining; Information retrieval","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":[0.06174359,0.001372828,0.001754049,0.005215576,0.002118109,0.006578426,0.004008481,0.002387921,0.003083409],"category_scores_gemma":[0.1326322,0.0007472845,0.001906858,0.004512619,0.00385549,0.01066455,0.00732428,0.004901134,0.000678383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005272149,"about_ca_system_score_gemma":0.00585695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002987583,"about_ca_topic_score_gemma":0.00219438,"domain_scores_codex":[0.9464905,0.03294298,0.003737311,0.004867974,0.01093001,0.00103129],"domain_scores_gemma":[0.8826864,0.06676891,0.009390525,0.01822527,0.02055236,0.002376545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001073434,0.0001080248,0.002343144,0.0002263899,0.0001232738,0.0001563744,0.0007789696,0.1774675,0.001170967,0.7460174,0.004799361,0.06670129],"study_design_scores_gemma":[0.00001681839,0.00007767821,0.0003206683,0.00007383407,0.00001924193,0.0001047508,0.0001695091,0.57372,0.001323811,0.4161219,0.008013183,0.00003850245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002658719,0.0001654039,0.9947278,0.000596144,0.00003929145,0.000154792,0.0002369966,0.0001650908,0.001255728],"genre_scores_gemma":[0.210242,0.0003979275,0.7842477,0.0004217813,0.0001908636,0.0009348217,0.001410472,0.0001954357,0.001958947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06174359,"threshold_uncertainty_score":0.3265352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09002040693682094,"score_gpt":0.3528462859397726,"score_spread":0.2628258790029516,"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."}}