{"id":"W2594736069","doi":"10.1109/access.2017.2678510","title":"LACS: A Lightweight Label-Based Access Control Scheme in IoT-Based 5G Caching Context","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Computer network; False sharing; Node (physics); Access control; Wireless; Wireless network; Context (archaeology); Verifiable secret sharing; Cache; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001490858,0.0006961391,0.0009140374,0.001119699,0.001646474,0.001659085,0.00195003,0.001199911,0.001069949],"category_scores_gemma":[0.00386123,0.0002234432,0.0005592369,0.0009147994,0.001510567,0.00302846,0.002195203,0.001135227,0.0003783054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002178462,"about_ca_system_score_gemma":0.001956068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006409136,"about_ca_topic_score_gemma":0.004213058,"domain_scores_codex":[0.9980501,0.0004354506,0.0001851518,0.0003157782,0.0006199442,0.0003936154],"domain_scores_gemma":[0.9966698,0.000546517,0.000466787,0.0009244022,0.001168967,0.0002234561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002846588,0.0006124018,0.009152606,0.000708209,0.0003017389,0.002801184,0.001860099,0.1069294,0.1945876,0.2987951,0.02139298,0.360012],"study_design_scores_gemma":[0.0001627036,0.0005688844,0.001073,0.00006321976,0.0001371813,0.001102057,0.0001731527,0.9208744,0.03196073,0.02693987,0.0167494,0.0001955119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09516652,0.001920254,0.8920774,0.0006280101,0.0003862376,0.0004298486,0.000163749,0.003633474,0.005594468],"genre_scores_gemma":[0.959313,0.0003062029,0.0377259,0.000285048,0.0001016198,0.0001288609,0.0001032249,0.00002939605,0.00200663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006409136,"threshold_uncertainty_score":0.0158059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05803462649450163,"score_gpt":0.3313925177185276,"score_spread":0.273357891224026,"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."}}