{"id":"W4406457730","doi":"10.1109/jiot.2024.3521609","title":"CLDP=FATD: Secure Federated Averaging Threat Detection Framework for Intelligent Vehicle Sensor Networks Based on Client-Level Differential Privacy","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differential privacy; Computer science; Computer security; Privacy protection; Differential (mechanical device); Internet privacy; Cryptography; Information privacy; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005884395,0.0002936018,0.0003664989,0.0003480887,0.0003855015,0.0007674373,0.0009625839,0.0002268282,0.000008195896],"category_scores_gemma":[0.0001980843,0.0002616317,0.0003221636,0.0003344219,0.00004158722,0.0003616381,0.000198578,0.0009663149,0.000004273506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288355,"about_ca_system_score_gemma":0.00008696474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002675003,"about_ca_topic_score_gemma":0.000001268848,"domain_scores_codex":[0.9978209,0.0001338501,0.0007032977,0.0004365652,0.0003800438,0.0005253031],"domain_scores_gemma":[0.9982262,0.0005257084,0.0004709725,0.0003150209,0.0003319312,0.0001301666],"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":[0.002772737,0.001596823,0.006056501,0.0005162163,0.001161801,0.0001662071,0.01394158,0.09828992,0.01957846,0.004793007,0.02663616,0.8244906],"study_design_scores_gemma":[0.0007101182,0.0002930712,0.0004355707,0.001061841,0.00002972094,0.00003582292,0.00002658918,0.9241964,0.06818926,0.004142504,0.0006507592,0.0002282916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2050639,0.00003751728,0.7750919,0.0004788841,0.01896066,0.0001912757,3.025527e-7,0.0000887768,0.00008679468],"genre_scores_gemma":[0.9634043,0.000007947149,0.03391259,0.0008983353,0.00155857,0.00000588294,0.000001691168,0.00002268106,0.0001880277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8259065,"threshold_uncertainty_score":0.9999836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536574780553387,"score_gpt":0.2791098737650856,"score_spread":0.2537441259595518,"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."}}