{"id":"W1977716721","doi":"10.1145/1533057.1533066","title":"Efficient IRM enforcement of history-based access control policies","year":2009,"lang":"en","type":"article","venue":"","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Enforcement; Rewriting; Computer science; Access control; Code (set theory); Computer security; Mandatory access control; Security policy; Binary code; Programming language; Binary number; Role-based access control; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002210482,0.00007727998,0.0001183785,0.0001082687,0.00005229301,0.00004524643,0.0008344609,0.00002778912,0.0001050381],"category_scores_gemma":[0.00002262236,0.0000695575,0.00005690917,0.0001716489,0.00003385533,0.00007050997,0.00004398028,0.0000475165,0.000007397983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160146,"about_ca_system_score_gemma":0.000106799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000133191,"about_ca_topic_score_gemma":0.000001480581,"domain_scores_codex":[0.9991489,0.00002970481,0.0002407892,0.0001790938,0.0002399463,0.000161532],"domain_scores_gemma":[0.9992537,0.00008084716,0.0001230338,0.0003927002,0.00009899355,0.00005066308],"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":[0.000008073101,0.0001766587,0.0002003183,0.00001092643,0.00000594734,7.062789e-7,0.0008028872,0.06836307,0.0009842342,0.921153,0.001883849,0.006410347],"study_design_scores_gemma":[0.0005854375,0.0001070386,0.00372297,0.00001349685,0.000003526441,8.719893e-7,0.00001342823,0.9752469,0.007652788,0.0006624167,0.01186938,0.0001217287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01384066,0.00009255319,0.9672089,0.001511295,0.0002720001,0.0001263537,8.056982e-7,0.0001301581,0.01681734],"genre_scores_gemma":[0.9896407,6.290606e-7,0.00687509,0.003385395,0.00002977905,0.000002819213,7.968716e-7,0.000001681575,0.00006309066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9758,"threshold_uncertainty_score":0.2836473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511116295329701,"score_gpt":0.2871634771442493,"score_spread":0.2520523141909523,"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."}}