{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04607296,0.001464703,0.001998229,0.003459315,0.001975434,0.01109774,0.00385076,0.002395582,0.002621586],"category_scores_gemma":[0.07236461,0.001637192,0.00256191,0.001908627,0.006760972,0.01435397,0.0115487,0.007656522,0.00106734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002865155,"about_ca_system_score_gemma":0.006421792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003419482,"about_ca_topic_score_gemma":0.001075653,"domain_scores_codex":[0.9301142,0.02164835,0.008512324,0.01078133,0.02519117,0.003752546],"domain_scores_gemma":[0.9022647,0.03008058,0.007479885,0.04613092,0.01182248,0.002221476],"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":[0.0004571636,0.0005811537,0.01211473,0.0005416424,0.0006339729,0.0006230566,0.004327911,0.06217629,0.02364943,0.6895804,0.002712859,0.2026014],"study_design_scores_gemma":[0.0001224927,0.0004605305,0.004616751,0.0004504562,0.0004733395,0.0008188004,0.0006976876,0.3169078,0.04270318,0.5907811,0.04169955,0.0002683573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01003731,0.0002573258,0.9846661,0.000558823,0.00005632502,0.0003491913,0.0000455131,0.001165659,0.002863711],"genre_scores_gemma":[0.5402235,0.0004899389,0.4528242,0.0007856304,0.0002897497,0.0007133778,0.0002430164,0.0006969944,0.003733719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04607296,"threshold_uncertainty_score":0.24366,"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."}}