{"id":"W4386528685","doi":"10.48550/arxiv.2309.02159","title":"The Adversarial Implications of Variable-Time Inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ben-Gurion University of the Negev; Defense Advanced Research Projects Agency; Government of Canada; Canadian Institute for Advanced Research; Alfred P. Sloan Foundation","keywords":"Computer science; Adversary; Inference; Exploit; Adversarial system; Information leakage; Side channel attack; Artificial intelligence; Machine learning; Computer security; Cryptography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006901355,0.0002831843,0.0003434188,0.0001983583,0.0004609414,0.0001291463,0.004436725,0.0002889749,0.00003091635],"category_scores_gemma":[0.0006081867,0.0002738707,0.0001897742,0.001172428,0.0002539394,0.0003157055,0.005355235,0.0008653467,0.0002164309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001706894,"about_ca_system_score_gemma":0.0005779737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003434305,"about_ca_topic_score_gemma":0.00002349535,"domain_scores_codex":[0.9979147,0.0002905115,0.0003258216,0.0009307538,0.0001395289,0.0003986507],"domain_scores_gemma":[0.9954714,0.001401746,0.0005480475,0.002158972,0.0003059133,0.0001139123],"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":[0.0000110895,0.00001622371,0.001072138,0.00001791789,0.00006445809,0.000009061219,0.00007683048,0.5316716,0.00003231408,0.4664149,0.0002892931,0.0003240928],"study_design_scores_gemma":[0.0003027235,0.00002684636,0.003224227,0.00007051863,0.00006864084,0.000001274289,0.00002820514,0.7235101,0.00001775223,0.2716503,0.0007916215,0.0003077777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004749324,0.00001936313,0.9893698,0.0006064813,0.00113769,0.0003111385,0.00002511957,0.0004282769,0.003352867],"genre_scores_gemma":[0.987861,0.0001095768,0.008097354,0.00003400359,0.0001427576,0.000002951263,0.00002180236,0.00002874773,0.003701824],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9831117,"threshold_uncertainty_score":0.9999713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07381278144022783,"score_gpt":0.2224600832109499,"score_spread":0.1486473017707221,"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."}}