{"id":"W2120314095","doi":"10.1002/sec.1070","title":"Protect biometric data with compound chaotic encryption","year":2014,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Regional Laboratory Medicine Program","funders":"","keywords":"Computer science; Encryption; Biometrics; Chaotic; Cryptosystem; Cipher; Cryptography; Theoretical computer science; Scrambling; Key space; Ciphertext; Symmetric-key algorithm; Data mining; Algorithm; Computer security; Artificial intelligence; Public-key cryptography","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":[0.0002873509,0.0003171246,0.0003837761,0.0004316689,0.0002574138,0.000469821,0.0003026002,0.0004549311,0.00197378],"category_scores_gemma":[0.0008857458,0.0001166485,0.0003972218,0.0004604946,0.0003244718,0.001081869,0.0008522799,0.0003422667,0.0006173057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002557785,"about_ca_system_score_gemma":0.0003174035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001577124,"about_ca_topic_score_gemma":0.00008685863,"domain_scores_codex":[0.999669,0.00005740155,0.00002627814,0.00005350341,0.0001603743,0.00003340057],"domain_scores_gemma":[0.9996589,0.00008212138,0.00006907153,0.0001113313,0.00006389316,0.00001462281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001155134,0.0001322402,0.002597207,0.0004235463,0.0001443318,0.0009670019,0.0003423944,0.07460848,0.6134479,0.09257188,0.002750453,0.2108594],"study_design_scores_gemma":[0.00009560156,0.000493443,0.002213659,0.0000460823,0.00007616389,0.002231538,0.00009635863,0.5858052,0.364714,0.03330702,0.01085621,0.00006458696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3051246,0.0006741626,0.6849527,0.000342329,0.0001269647,0.000109221,0.0001867034,0.0005017391,0.007981549],"genre_scores_gemma":[0.9432212,0.0002915331,0.05272282,0.00005254443,0.00003722385,0.00003745167,0.0001146508,0.00002859866,0.003493951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00197378,"threshold_uncertainty_score":0.006602943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988761117961703,"score_gpt":0.2406658426381366,"score_spread":0.2207782314585195,"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."}}