{"id":"W2109468254","doi":"10.1109/tkde.2012.86","title":"Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Technische Universität Wien","keywords":"Computer science; Null (SQL); Secrecy; Tuple; SQL; Semantics (computer science); Relational database; Information retrieval; Theoretical computer science; Database; Data mining; Programming language; Computer security; Mathematics","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":[0.009718719,0.00083607,0.001011312,0.001406261,0.001749695,0.007988882,0.002574933,0.001401307,0.00158052],"category_scores_gemma":[0.01886802,0.0009864589,0.002204715,0.00176721,0.007563262,0.01994334,0.008944551,0.003789786,0.0005160403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001458883,"about_ca_system_score_gemma":0.002978869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754887,"about_ca_topic_score_gemma":0.001491387,"domain_scores_codex":[0.9881644,0.004122072,0.00125864,0.001439778,0.003990895,0.00102419],"domain_scores_gemma":[0.9814098,0.006825322,0.001493824,0.007600015,0.00213544,0.0005356278],"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":[0.0001734016,0.00005596434,0.000852195,0.0000658881,0.00003699302,0.0001915758,0.0009607816,0.008203553,0.003223787,0.968259,0.001159872,0.01681685],"study_design_scores_gemma":[0.00006318793,0.00009548402,0.0001643835,0.00004316586,0.00008854787,0.000314434,0.0003222918,0.07279181,0.01576312,0.9010776,0.009224696,0.00005132245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0313521,0.0002118035,0.9611979,0.001109178,0.00005680165,0.00009198327,0.0002114376,0.0007697699,0.004999137],"genre_scores_gemma":[0.7206561,0.0006648685,0.2711501,0.0008319452,0.0002600551,0.0002869899,0.0005196398,0.0004063641,0.00522398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009718719,"threshold_uncertainty_score":0.0513981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06739210014020883,"score_gpt":0.3002273917608937,"score_spread":0.2328352916206849,"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."}}