{"id":"W4367682491","doi":"10.1016/j.engappai.2023.106220","title":"Learning asymmetric encryption using adversarial neural networks","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Japan Society for the Promotion of Science; Mitacs; Telecommunications Advancement Foundation; Kyushu University; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Computer science; Communication source; Alice and Bob; Plaintext; Encryption; Computer security; Public-key cryptography; Secure communication; Computer network; Alice (programming language); Artificial neural network; Information leakage; Artificial intelligence","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":[0.0005658641,0.0001680803,0.0001913753,0.0006169404,0.0002148813,0.00008217265,0.0008326287,0.000100417,0.000007544776],"category_scores_gemma":[0.0003715822,0.0001990147,0.0000900074,0.004087009,0.00004706147,0.0003510081,0.0002975991,0.0004187626,0.00005168981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006529654,"about_ca_system_score_gemma":0.00003030998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005710253,"about_ca_topic_score_gemma":9.239997e-7,"domain_scores_codex":[0.9984435,0.00004995425,0.0004670933,0.0003905137,0.0002926842,0.00035623],"domain_scores_gemma":[0.9987838,0.000379048,0.0001797587,0.0004537134,0.0001235327,0.00008012892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002496367,0.00001249899,0.00006956579,0.000008445655,0.000008218828,0.000001296565,0.0001423965,0.8253288,0.001197634,0.04649301,0.000002817434,0.1267328],"study_design_scores_gemma":[0.00001829352,0.0000311842,0.0001362493,0.00001240276,0.000008882861,0.000003729154,0.00008116309,0.9961969,0.001974171,0.001161202,0.0001978621,0.0001780074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01060556,0.00004361521,0.9878144,0.00007408904,0.0004646343,0.0002404527,7.44325e-7,0.0006955169,0.00006099298],"genre_scores_gemma":[0.9153713,0.00001414727,0.08422151,0.000005940286,0.0002969073,0.00004440321,0.000008455586,0.00002209717,0.0000152071],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9047658,"threshold_uncertainty_score":0.8115584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0242679939324492,"score_gpt":0.2830559774342701,"score_spread":0.2587879835018209,"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."}}