{"id":"W2137344466","doi":"10.1504/ijbidm.2006.009135","title":"A unified framework for protecting sensitive association rules in business collaboration","year":2006,"lang":"en","type":"article","venue":"International Journal of Business Intelligence and Data Mining","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Association rule learning; Set (abstract data type); Process (computing); Information sensitivity; Data mining; Knowledge management; Data science; Computer security","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":[0.02927232,0.001600822,0.003842584,0.006162829,0.003456886,0.01203475,0.007060817,0.005769329,0.002046121],"category_scores_gemma":[0.04416708,0.001430115,0.004589219,0.006963862,0.006416206,0.02079553,0.01150103,0.006807464,0.001130667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00213339,"about_ca_system_score_gemma":0.006494875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002249816,"about_ca_topic_score_gemma":0.001592479,"domain_scores_codex":[0.9656318,0.01322834,0.003648493,0.004374621,0.01167047,0.001446233],"domain_scores_gemma":[0.9538376,0.01489578,0.003669426,0.02122777,0.00505554,0.001313927],"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":[0.0002660617,0.0003928412,0.001102884,0.0003087685,0.000294508,0.0003938476,0.0009462657,0.1032266,0.005909535,0.6920055,0.004011159,0.1911421],"study_design_scores_gemma":[0.000116025,0.0002579493,0.0003154665,0.0001193078,0.0002048125,0.0008621162,0.0002320254,0.4674394,0.00921968,0.5065296,0.01454997,0.0001537206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001311855,0.0001536261,0.9974946,0.000281056,0.00002250043,0.00009434086,0.00003912411,0.0002168916,0.0003860359],"genre_scores_gemma":[0.05551614,0.0003637445,0.9424336,0.0002145524,0.0001267393,0.0002997745,0.0002138623,0.00006140959,0.000770186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02927232,"threshold_uncertainty_score":0.1548086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06062454961180078,"score_gpt":0.3399472217237445,"score_spread":0.2793226721119437,"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."}}