{"id":"W3212670429","doi":"10.18280/ijsse.110504","title":"Coupling of Inference and Access Controls to Ensure Privacy Protection","year":2021,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Access control; Inference; Computer science; Computer security; Data access; Obligation; Data mining; Risk analysis (engineering); Database; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001379662,0.00005985349,0.0001859958,0.0001813581,0.00002515288,0.0002052447,0.0003419509,0.00002834521,0.00002931444],"category_scores_gemma":[0.002897372,0.00005109144,0.00003888158,0.0001484854,0.0000162068,0.0005594133,0.0002904928,0.0001282242,7.91302e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002072398,"about_ca_system_score_gemma":0.00003002564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000011603,"about_ca_topic_score_gemma":0.000009216351,"domain_scores_codex":[0.998646,0.00001792569,0.0005299459,0.0001152273,0.0006245957,0.00006636591],"domain_scores_gemma":[0.998592,0.0003598235,0.0002106789,0.00008854149,0.0006789308,0.00007007226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001915824,0.0005601321,0.01333953,0.000340998,0.001199855,0.000443882,0.01551909,0.4106765,0.06029078,0.1667025,0.0004669148,0.328544],"study_design_scores_gemma":[0.006387023,0.0005918522,0.2821476,0.002117098,0.0001295101,0.0007499818,0.004018024,0.3600509,0.03064698,0.04542171,0.2668412,0.0008981508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6829627,0.0003012981,0.3125145,0.003416024,0.0005757094,0.0001003282,0.00003449465,0.000005510977,0.00008944821],"genre_scores_gemma":[0.9987903,0.0002631114,0.0007609305,0.00007651417,0.00008876367,0.000001113893,0.0000014253,0.000002459827,0.00001540302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3276458,"threshold_uncertainty_score":0.3468634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07465132137388023,"score_gpt":0.3849149002359968,"score_spread":0.3102635788621166,"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."}}