{"id":"W2538151208","doi":"10.1109/wi-iatw.2006.6","title":"A Bayesian Network Approach to Detecting Privacy Intrusion","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Insider; Private information retrieval; Computer science; Insider threat; Information privacy; Computer security; Privacy software; Personally identifiable information; Internet privacy; Privacy by Design; Information sensitivity; Intrusion detection system; Intrusion; Bayesian network; Privacy policy; 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":[0.004802188,0.001117211,0.001500511,0.004299553,0.001022694,0.002264111,0.002393598,0.0023839,0.002174283],"category_scores_gemma":[0.01684095,0.000911184,0.001280117,0.002511706,0.001496292,0.004386167,0.001399527,0.002188392,0.0005045761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00231808,"about_ca_system_score_gemma":0.001762068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01251785,"about_ca_topic_score_gemma":0.00940145,"domain_scores_codex":[0.9958951,0.001967489,0.0001971419,0.0007007588,0.00103559,0.0002038434],"domain_scores_gemma":[0.9920982,0.006077288,0.0004844712,0.000284206,0.0008918525,0.0001640203],"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":[0.0002561495,0.0002189886,0.006887359,0.0002654569,0.0004111898,0.0003041498,0.0003643484,0.6077871,0.002344551,0.1424432,0.005581637,0.2331359],"study_design_scores_gemma":[0.00001643775,0.00002070548,0.0006314965,0.00002220192,0.00004657138,0.00009031929,0.00002444807,0.9507206,0.0004378539,0.04622611,0.001737063,0.00002617777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004033521,0.0004693198,0.9931859,0.0004500237,0.00002732352,0.00004639933,0.00009855271,0.0001909079,0.001498165],"genre_scores_gemma":[0.3648408,0.002045321,0.6260252,0.0003720164,0.000372701,0.0003734505,0.0005560758,0.00008678112,0.005327655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01251785,"threshold_uncertainty_score":0.02539665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010611613629996,"score_gpt":0.2137105615948139,"score_spread":0.2030989479648179,"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."}}