{"id":"W2414074698","doi":"10.1002/sec.1453","title":"Two‐level security for message sequences","year":2016,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eavesdropping; Adversary; Computer security; Construct (python library); Encryption; Key (lock); Computer security model; Sequence (biology); Provable security; Information-theoretic security; Theoretical computer science; Computer network","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":[0.004007927,0.0009191516,0.0009569952,0.0009866331,0.001048257,0.003859327,0.001463978,0.00161565,0.005443404],"category_scores_gemma":[0.009490893,0.0006317626,0.001259989,0.0008583897,0.003313624,0.00987342,0.003815311,0.003805854,0.001242013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002233982,"about_ca_system_score_gemma":0.001325191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003631051,"about_ca_topic_score_gemma":0.0002494189,"domain_scores_codex":[0.9946156,0.001576043,0.0005401685,0.0008409221,0.001606767,0.0008203579],"domain_scores_gemma":[0.9879402,0.005894142,0.001499531,0.003083987,0.00100384,0.0005781928],"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":[0.0004336936,0.0001200346,0.0004696349,0.0002712823,0.00003746915,0.0002228632,0.0004629802,0.04900476,0.01464218,0.914772,0.001209588,0.0183535],"study_design_scores_gemma":[0.0001207208,0.0002373815,0.000194758,0.00007190009,0.00002434617,0.0002592713,0.00008814761,0.2811082,0.01513206,0.695132,0.007569947,0.00006122034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08328568,0.000287052,0.9050522,0.001464998,0.00008978171,0.0001574718,0.0001432072,0.0005368906,0.008982732],"genre_scores_gemma":[0.8467428,0.0002719035,0.1458026,0.0003001302,0.0001189556,0.0002536564,0.0002627221,0.0001231742,0.006124218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005443404,"threshold_uncertainty_score":0.02119619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03194205530987518,"score_gpt":0.298445259408869,"score_spread":0.2665032040989938,"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."}}