{"id":"W1921007510","doi":"10.3233/jcs-2008-16203","title":"Privacy policy enforcement in enterprises with identity management solutions","year":2008,"lang":"en","type":"article","venue":"Journal of Computer Security","topic":"Access Control and Trust","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hewlett-Packard (Canada)","funders":"","keywords":"Privacy policy; Enforcement; Personally identifiable information; Privacy by Design; Leverage (statistics); Identity management; Information privacy; Privacy software; Computer security; Internet privacy; Business; Identity (music); Law enforcement; Computer science; Business process; Knowledge management; Access control; Work in process; Law; Marketing","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":[0.02357805,0.0004837891,0.0007730334,0.001073688,0.003377739,0.009514896,0.002784445,0.004084264,0.002394559],"category_scores_gemma":[0.02593108,0.001068436,0.001152377,0.001580966,0.004855406,0.01315597,0.006878046,0.005116381,0.001242446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002099591,"about_ca_system_score_gemma":0.006603108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003343879,"about_ca_topic_score_gemma":0.001729833,"domain_scores_codex":[0.9747812,0.01027911,0.002441184,0.002927605,0.007677633,0.001893247],"domain_scores_gemma":[0.9742739,0.008521017,0.002792499,0.01098771,0.002686731,0.0007382217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003067516,0.0007790885,0.008107673,0.0003801324,0.0001472487,0.001055789,0.006893981,0.02123027,0.01339513,0.7336003,0.007092066,0.2070116],"study_design_scores_gemma":[0.0002511792,0.0002685463,0.002203664,0.0005429002,0.0001950697,0.001254049,0.00292007,0.3006286,0.04696235,0.4921314,0.1524812,0.0001609252],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0348967,0.000336686,0.9428911,0.004244248,0.00008082853,0.0005637758,0.00004987324,0.002287563,0.01464918],"genre_scores_gemma":[0.3928041,0.0004004885,0.5971167,0.0009867965,0.00008118779,0.0003996828,0.0001514909,0.0002124026,0.007847195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02357805,"threshold_uncertainty_score":0.1246941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129607538769854,"score_gpt":0.3022430145189302,"score_spread":0.2809469391312317,"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."}}