{"id":"W2159826988","doi":"10.1016/j.jbi.2014.04.002","title":"Small sum privacy and large sum utility in data publishing","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Chinese University of Hong Kong; University Grants Committee","keywords":"Computer science; Data publishing; Aggregate (composite); Inference; Publishing; Contrast (vision); Point (geometry); Limit (mathematics); Data mining; Information retrieval; Theoretical computer science; Artificial intelligence; Mathematics","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":[0.01801707,0.001130377,0.002971509,0.001893218,0.002487702,0.009459536,0.003808015,0.0029736,0.003335485],"category_scores_gemma":[0.07814679,0.001170228,0.001793221,0.005293851,0.008610096,0.0207655,0.007024613,0.00747761,0.0008691305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0037878,"about_ca_system_score_gemma":0.003312473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009570624,"about_ca_topic_score_gemma":0.0007829461,"domain_scores_codex":[0.9807169,0.01115764,0.001074803,0.002153531,0.003810549,0.001086615],"domain_scores_gemma":[0.8891793,0.0861816,0.003097542,0.01668975,0.003443879,0.001407805],"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":[0.0005028784,0.0001192864,0.001259346,0.0002064027,0.00009064575,0.0001877685,0.0003601187,0.0554249,0.0007999811,0.8983989,0.003288706,0.0393611],"study_design_scores_gemma":[0.0000291993,0.000047187,0.0001377305,0.00002807397,0.00003226075,0.0001466286,0.00005911113,0.1317166,0.0009682463,0.865212,0.00160574,0.00001720209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02615958,0.002113855,0.9576331,0.006397857,0.0002462588,0.00008766246,0.0003015915,0.0002345886,0.006825575],"genre_scores_gemma":[0.8326494,0.002557008,0.1507971,0.001412825,0.001098462,0.0003167815,0.0004270291,0.0002643117,0.01047699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01801707,"threshold_uncertainty_score":0.09528452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07462756420281043,"score_gpt":0.2990345094568255,"score_spread":0.2244069452540151,"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."}}