{"id":"W2912455255","doi":"10.1109/tcc.2018.2866405","title":"Efficient and Privacy-Preserving Online Fingerprint Authentication Scheme over Outsourced Data","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Biometrics; Computer security; Encryption; Fingerprint (computing); Authentication (law); Information privacy; Server; Homomorphic encryption; Cryptography; Computer network","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":[0.0006357929,0.0001613579,0.000155206,0.0003252145,0.0005459634,0.0002931319,0.001560377,0.0000821841,0.00003487485],"category_scores_gemma":[0.00006090606,0.0001658393,0.00004578519,0.001052792,0.0001076979,0.0001326898,0.000119017,0.00026044,0.0000528413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005908046,"about_ca_system_score_gemma":0.0000484437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007564981,"about_ca_topic_score_gemma":0.00001884077,"domain_scores_codex":[0.9981487,0.00009260607,0.0003674969,0.000736245,0.0003697729,0.0002851837],"domain_scores_gemma":[0.997695,0.0001980577,0.0001458465,0.001691372,0.0001457554,0.0001239196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009831085,0.004190935,0.0006390911,0.0002902012,0.0003913375,0.00001476375,0.02709113,0.01484183,0.03997755,0.01120227,0.002361022,0.8989016],"study_design_scores_gemma":[0.0003086025,0.00003810459,0.003559342,0.00004460554,0.00001169198,0.000008553999,0.0000481544,0.992184,0.002203818,0.0001148425,0.001296637,0.0001816721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3619953,0.0000219999,0.6361762,0.000605586,0.0008503863,0.0001176551,0.00001406895,0.000183291,0.00003557007],"genre_scores_gemma":[0.940879,0.000003810248,0.0586357,0.0002150135,0.0001728489,0.000001756755,0.000007501944,0.00001155657,0.00007279273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9773421,"threshold_uncertainty_score":0.6762732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05653985901422063,"score_gpt":0.3123942270933521,"score_spread":0.2558543680791315,"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."}}