{"id":"W3048030299","doi":"10.1109/access.2020.3015099","title":"Memory-Efficient Random Order Exponentiation Algorithm","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Canada Research Chairs","keywords":"Modular exponentiation; Exponentiation; Computer science; Side channel attack; Cryptosystem; Algorithm; Power analysis; Public-key cryptography; Cryptography; Modular arithmetic; Random number generation; Theoretical computer science; Arithmetic; Encryption; Mathematics; Computer security","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":[0.0001641009,0.0000977204,0.0001177912,0.00007957892,0.0001643103,0.0003966412,0.0007932237,0.00002840711,0.0001088555],"category_scores_gemma":[0.0000217515,0.0000910554,0.0000591644,0.0009286191,0.00002073035,0.0005156069,0.0001545973,0.00008189904,0.00003909209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000120929,"about_ca_system_score_gemma":0.00003933078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007014328,"about_ca_topic_score_gemma":0.000008737188,"domain_scores_codex":[0.9989344,0.00005540867,0.0002173343,0.0003059982,0.0002915302,0.0001953208],"domain_scores_gemma":[0.9993972,0.00004803422,0.00008086174,0.0002514452,0.0001188172,0.0001036554],"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":[0.00006153081,0.0005159649,0.0008556749,0.00007098139,0.0001431048,0.00006523781,0.01663665,0.0119279,0.002880321,0.01111402,0.02543252,0.9302961],"study_design_scores_gemma":[0.003211564,0.00006148039,0.002043766,0.000006904766,0.00001790157,0.000005517998,0.0001432836,0.969429,0.01718579,0.001195511,0.006364375,0.0003349222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04602712,0.00004685223,0.950491,0.002171871,0.0005770142,0.0002296593,0.000006278376,0.0001429405,0.000307347],"genre_scores_gemma":[0.9768736,0.00001315853,0.02116722,0.001720956,0.0001655421,0.00003424861,0.00001029204,0.00000702508,0.000007956019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9575011,"threshold_uncertainty_score":0.3824821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03840300572360002,"score_gpt":0.3063297767279557,"score_spread":0.2679267710043557,"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."}}