{"id":"W2133667521","doi":"10.1109/acssc.2008.5074642","title":"Secure methods for fuzzy key binding in biometric authentication applications","year":2008,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Robustness (evolution); Flexibility (engineering); Cryptography; Fuzzy logic; Computer security; Key (lock); Quantization (signal processing); Authentication (law); Data mining; Artificial intelligence; Mathematics; Algorithm; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001784058,0.0004582288,0.000528391,0.0007510361,0.001087509,0.001626577,0.001076599,0.001543341,0.009359869],"category_scores_gemma":[0.004545529,0.0003505253,0.0005199416,0.0006839324,0.001922762,0.001966895,0.001645103,0.001974953,0.002337946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009999775,"about_ca_system_score_gemma":0.0008466049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007551238,"about_ca_topic_score_gemma":0.001026249,"domain_scores_codex":[0.9982641,0.0004199526,0.0001026497,0.0002113677,0.0009174906,0.00008440502],"domain_scores_gemma":[0.9987211,0.0006553273,0.00009944791,0.0002938465,0.0001955835,0.00003471923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000170791,0.00005456475,0.0001348125,0.0001497945,0.00002712903,0.0001719941,0.0003255334,0.03943576,0.01814375,0.8027931,0.001966394,0.1366265],"study_design_scores_gemma":[0.00008458192,0.0001151843,0.0001882603,0.00008743212,0.00002863104,0.0002844883,0.0001154409,0.4590002,0.01810736,0.499533,0.02240334,0.00005214461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004285071,0.000437202,0.9878069,0.0003493977,0.00006925032,0.00007205614,0.00002162334,0.0001364696,0.006822031],"genre_scores_gemma":[0.4155648,0.00148448,0.5618407,0.0002838733,0.0001763982,0.0003761927,0.00008209365,0.00009214725,0.02009932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009359869,"threshold_uncertainty_score":0.03131187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05352405476475842,"score_gpt":0.3573716819666486,"score_spread":0.3038476272018902,"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."}}