{"id":"W4285230782","doi":"10.1109/tcsi.2022.3176966","title":"DPCrypto: Acceleration of Post-Quantum Cryptography Using Dot-Product Instructions on GPUs","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea","keywords":"Computer science; Matrix multiplication; Dot product; Parallel computing; Cryptography; Multiplication (music); Convolution (computer science); Throughput; Key exchange; Polynomial; Computational science; Theoretical computer science; Encryption; Algorithm; Public-key cryptography; Mathematics; Quantum","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004181071,0.0002480864,0.0003233247,0.0006803712,0.001197238,0.0001750396,0.0004478809,0.00007114177,0.00003985562],"category_scores_gemma":[0.000007022227,0.0002519126,0.0002292027,0.001248693,0.0001265309,0.0004474301,0.000009225086,0.0003560149,0.000001728401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006736638,"about_ca_system_score_gemma":0.0001062424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002433419,"about_ca_topic_score_gemma":0.00001704138,"domain_scores_codex":[0.9976549,0.0002920955,0.0004670521,0.0006651804,0.000622761,0.0002980567],"domain_scores_gemma":[0.9985953,0.00007933932,0.0002060893,0.0008346381,0.0001225695,0.0001620876],"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":[0.0001221998,0.001345924,0.0001657568,0.0003976909,0.0005665322,0.0000342321,0.004494117,0.1011895,0.3896926,0.3966813,0.0002615882,0.1050486],"study_design_scores_gemma":[0.0235364,0.01952044,0.01610119,0.00256069,0.001879448,0.00760968,0.039104,0.5002589,0.2381407,0.02419159,0.1137768,0.01332015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4743231,0.0005098699,0.5185351,0.0002169557,0.004424223,0.000785684,0.0004058108,0.0002039172,0.0005953628],"genre_scores_gemma":[0.999084,0.00006521357,0.0005842198,0.0001053441,0.0000552694,0.00006591347,0.0000123228,0.00001887686,0.000008847579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.524761,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537184136445389,"score_gpt":0.2331009502369214,"score_spread":0.2077291088724675,"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."}}