{"id":"W2866028610","doi":"10.1109/eurosp.2018.00032","title":"CRYSTALS - Kyber: A CCA-Secure Module-Lattice-Based KEM","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":1000,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Government of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Bpifrance","keywords":"Key encapsulation; Computer science; Random oracle; NIST; Key exchange; Learning with errors; Cryptographic primitive; Cryptography; Ciphertext; Encryption; Theoretical computer science; Public-key cryptography; Cryptographic protocol; 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":[0.001695709,0.0005896116,0.001032228,0.0007140596,0.001621736,0.002210905,0.002129231,0.001275767,0.008308574],"category_scores_gemma":[0.003636133,0.0005188234,0.001113465,0.001097927,0.002791462,0.005807342,0.006555586,0.003724694,0.005889385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009805682,"about_ca_system_score_gemma":0.002843514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004385175,"about_ca_topic_score_gemma":0.0006307113,"domain_scores_codex":[0.9975469,0.0005652522,0.0001461534,0.0003822953,0.00103755,0.0003219015],"domain_scores_gemma":[0.998583,0.0003604761,0.0001360498,0.0005175698,0.0002155277,0.0001873173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004725125,0.0001845296,0.0003498267,0.0002929996,0.00005735476,0.0003310972,0.0003882212,0.01921092,0.01602079,0.8885043,0.01164973,0.06253771],"study_design_scores_gemma":[0.0002634551,0.0002897675,0.0002072442,0.00008220971,0.0000543485,0.0007393028,0.0001512719,0.1462456,0.05302723,0.7274542,0.07129535,0.0001900743],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02848282,0.0004009776,0.9361516,0.001038487,0.0003665579,0.0004025886,0.0005943663,0.003676108,0.02888652],"genre_scores_gemma":[0.532029,0.000550199,0.4282984,0.00097857,0.0002075005,0.0006921177,0.001113956,0.000853515,0.03527673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008308574,"threshold_uncertainty_score":0.0277949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456623467212205,"score_gpt":0.2615881485768737,"score_spread":0.2370219139047516,"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."}}