{"id":"W2080756653","doi":"10.1109/icinfa.2006.374170","title":"Efficient Sanitization of Informative Association Rules with Updates","year":2006,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Association rule learning; Computer science; Data mining; Set (abstract data type); Database; Association (psychology); Data set; Information retrieval; Artificial intelligence","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":[0.004168284,0.001070227,0.002442974,0.002690559,0.001013995,0.002715632,0.002882339,0.0008533067,0.001078733],"category_scores_gemma":[0.01975948,0.001045684,0.001488358,0.002380773,0.001116799,0.004005141,0.002768666,0.002498252,0.0007670149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003933781,"about_ca_system_score_gemma":0.00195692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001091022,"about_ca_topic_score_gemma":0.001249177,"domain_scores_codex":[0.993542,0.001090376,0.0008785171,0.001108135,0.003001191,0.0003798677],"domain_scores_gemma":[0.9813012,0.005955867,0.001867097,0.008272502,0.002372768,0.0002306138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009205465,0.0004838895,0.01009248,0.0003211989,0.0003703047,0.000801652,0.0007759489,0.06970514,0.04505781,0.02382146,0.004327211,0.8433223],"study_design_scores_gemma":[0.0001524412,0.000325264,0.002180825,0.00006029002,0.0002862329,0.001394805,0.0002985228,0.8528699,0.08617348,0.04370631,0.01247024,0.00008160881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05740061,0.000522613,0.9382313,0.0002514441,0.00006991118,0.000231913,0.0001769947,0.00218199,0.0009333108],"genre_scores_gemma":[0.3506348,0.0004024468,0.6450481,0.0002197403,0.0001283532,0.0002346671,0.0009753798,0.0001632872,0.002193209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004168284,"threshold_uncertainty_score":0.0220443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00711659574463585,"score_gpt":0.2119219276279817,"score_spread":0.2048053318833458,"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."}}