{"id":"W1985103528","doi":"10.1109/pst.2010.5593246","title":"Security of Error Correcting Code for biometric Encryption","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Privacy Analytics (Canada)","funders":"","keywords":"Computer science; Encryption; Biometrics; Theoretical computer science; Generalization; Code (set theory); Cryptography; Hadamard transform; Algorithm; Error detection and correction; Key (lock); Block (permutation group theory); Relation (database); Computer security; Mathematics; Data mining; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007197172,0.0000636089,0.0001066207,0.0008508607,0.0000828891,0.00006917235,0.0005154916,0.00007988289,0.00005828525],"category_scores_gemma":[0.0005166891,0.00005787575,0.00006921137,0.003229886,0.0000376449,0.0002462858,0.00007802955,0.0001023053,0.00001979643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001178831,"about_ca_system_score_gemma":0.00003962408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005371516,"about_ca_topic_score_gemma":0.0000857511,"domain_scores_codex":[0.9991749,0.00001903016,0.0002355387,0.0002372589,0.0001898532,0.0001434432],"domain_scores_gemma":[0.9989631,0.0002130959,0.0001367488,0.0003648436,0.0002624086,0.000059797],"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":[0.00001495509,0.0006414774,0.005217392,0.0001461668,0.00003235187,6.997529e-7,0.00187803,0.000002287046,0.1319553,0.4799898,0.01017118,0.3699504],"study_design_scores_gemma":[0.001468112,0.0002426927,0.02780952,0.00001192601,0.0000212551,0.00003866689,0.0002817216,0.4045368,0.4347484,0.02819998,0.1020233,0.0006177081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1925442,0.00001912342,0.803977,0.0003017159,0.001598168,0.0001870223,0.00001370532,0.0001096096,0.001249429],"genre_scores_gemma":[0.9418635,0.000001970174,0.05775272,0.00005860195,0.00003713936,0.00001032039,0.000005497152,0.00000309054,0.0002671485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7493193,"threshold_uncertainty_score":0.2360104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04606021919436584,"score_gpt":0.3177634592881938,"score_spread":0.271703240093828,"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."}}