{"id":"W3016801111","doi":"10.1007/978-3-030-45371-8_8","title":"Lempel-Ziv Compression with Randomized Input-Output for Anti-compression Side-Channel Attacks Under HTTPS/TLS","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Side channel attack; Compression (physics); Timing attack; Data compression; Channel (broadcasting); Compression ratio; Computer security; Algorithm; Computer network; Cryptography","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":[0.00199115,0.001644836,0.001413719,0.001333328,0.001199169,0.002787194,0.001683498,0.002805639,0.01158289],"category_scores_gemma":[0.006109706,0.0006994575,0.0009364545,0.001309562,0.002725732,0.003954376,0.004809218,0.003709145,0.005475724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001530872,"about_ca_system_score_gemma":0.001660209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002865225,"about_ca_topic_score_gemma":0.0004410063,"domain_scores_codex":[0.99593,0.000984903,0.0001932806,0.0004030778,0.00185411,0.0006345732],"domain_scores_gemma":[0.9974329,0.001133968,0.0001805032,0.0009273121,0.0002426816,0.00008271666],"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":[0.001046568,0.0002757559,0.000553418,0.0005644479,0.0001190719,0.0006946708,0.0002925323,0.04564704,0.03114916,0.6813865,0.02636361,0.2119073],"study_design_scores_gemma":[0.0001591719,0.0003071659,0.0003387296,0.0002627532,0.00008763035,0.001197309,0.0001023643,0.399935,0.06303951,0.5126461,0.02181958,0.0001047837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0388979,0.002702112,0.8730195,0.003217956,0.0006469847,0.0004940975,0.0005207225,0.006983102,0.07351772],"genre_scores_gemma":[0.799408,0.001476644,0.1596728,0.001541027,0.0006381131,0.0006035452,0.0008644866,0.0007832035,0.03501223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01158289,"threshold_uncertainty_score":0.03874862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959906724605882,"score_gpt":0.2830922823198321,"score_spread":0.2534932150737733,"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."}}