{"id":"W2461678157","doi":"10.1007/978-3-319-49175-2_7","title":"Detecting Algebraic Manipulation in Leaky Storage Systems","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Code word; Adversary; Code (set theory); Upper and lower bounds; Algebraic number; Binary logarithm; Discrete mathematics; Computer science; Mathematics; Combinatorics; Algorithm; Decoding methods; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000606939,0.00030926,0.0003424519,0.0008492933,0.00007343078,0.0001501998,0.001033622,0.0003186779,0.0000104918],"category_scores_gemma":[0.00004532647,0.0002977631,0.00004691701,0.0002697471,0.0001689365,0.0002981052,0.0002914451,0.0006923021,0.00002260155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006446527,"about_ca_system_score_gemma":0.00005427524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002254648,"about_ca_topic_score_gemma":0.0001769583,"domain_scores_codex":[0.9983358,0.00002893592,0.0004499788,0.0004549024,0.0003923877,0.0003380012],"domain_scores_gemma":[0.9985615,0.0003140712,0.0001157617,0.0008816516,0.0000735065,0.00005353474],"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":[0.000003207983,0.00000962027,0.0001751511,0.0002210207,0.00001028036,0.00003378551,0.001016089,0.4933702,0.00230002,0.01389778,0.000009939324,0.4889529],"study_design_scores_gemma":[0.0001557461,0.00003088592,0.0002468696,0.00173417,0.000003415015,0.00002925139,3.531611e-7,0.9663229,0.002303932,0.02753278,0.001019229,0.0006204542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001659722,0.001036481,0.9913926,0.00005462557,0.0007051487,0.0003627262,0.000002838202,0.0005236366,0.004262267],"genre_scores_gemma":[0.9831071,0.00008153805,0.01644343,0.00004305883,0.0001861421,0.00001779938,0.000002897697,0.00005759908,0.00006042335],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9814474,"threshold_uncertainty_score":0.9999474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01963889144653031,"score_gpt":0.2364708196744764,"score_spread":0.2168319282279461,"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."}}