{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005338315,0.0005194229,0.0005383947,0.0006672057,0.0004585517,0.001042525,0.001049039,0.0005632411,0.006512603],"category_scores_gemma":[0.002405812,0.0002207598,0.0004335433,0.0007452108,0.0004632803,0.001393261,0.001066427,0.0007121653,0.003682186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503342,"about_ca_system_score_gemma":0.001323289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005898469,"about_ca_topic_score_gemma":0.0008679227,"domain_scores_codex":[0.9990364,0.000154393,0.0001254953,0.0002288684,0.0002902717,0.0001646093],"domain_scores_gemma":[0.9991099,0.0001952295,0.0001251503,0.0003411782,0.0001971292,0.00003131271],"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":[0.001217097,0.0001805597,0.001902514,0.0003176865,0.00009052802,0.0003544493,0.0002473956,0.0498359,0.05781703,0.1231203,0.01176993,0.7531467],"study_design_scores_gemma":[0.0007034779,0.0009005577,0.00124617,0.0001542753,0.0001464714,0.001766384,0.0001323465,0.681379,0.1540421,0.08150542,0.07787924,0.0001446355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03316273,0.0007648664,0.9499099,0.0003720984,0.0001463086,0.000241572,0.0002064573,0.002919246,0.01227675],"genre_scores_gemma":[0.4324026,0.0004750626,0.5486134,0.0003048548,0.00009420554,0.0002967461,0.0005641258,0.0002301636,0.01701884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006512603,"threshold_uncertainty_score":0.02178687,"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."}}