{"id":"W4416873169","doi":"10.1109/tkde.2025.3638821","title":"A Log-Likelihood Chain Framework for Defending Against LDP Data Poisoning Attacks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Categorical variable; Anomaly detection; Intrusion detection system; Skew; Data modeling; Privacy protection; Denial-of-service attack","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":[0.006393286,0.001434418,0.002197145,0.002843699,0.0009679252,0.00301312,0.003102125,0.002302438,0.003286299],"category_scores_gemma":[0.01844927,0.0008013571,0.001233246,0.002097584,0.002317849,0.005224243,0.004248424,0.003307662,0.001819046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397162,"about_ca_system_score_gemma":0.001965656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002737468,"about_ca_topic_score_gemma":0.002242982,"domain_scores_codex":[0.9957817,0.001705522,0.0001999576,0.0008141427,0.001166433,0.0003323976],"domain_scores_gemma":[0.9893928,0.006648409,0.001149568,0.001355838,0.001076038,0.0003773481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006902051,0.0002691992,0.009126791,0.0002792774,0.0002428158,0.0005950811,0.0003603026,0.6719401,0.005833285,0.07944053,0.005799396,0.225423],"study_design_scores_gemma":[0.00001604311,0.00004227913,0.0001990315,0.00001007428,0.00001210574,0.00007442635,0.00001368346,0.9782192,0.0008470714,0.0196108,0.0009387647,0.00001646276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006833178,0.0004398589,0.990535,0.0002655738,0.00002750372,0.0000602179,0.0001120278,0.0007717078,0.000954825],"genre_scores_gemma":[0.6640633,0.001083834,0.3241925,0.0005349479,0.000429921,0.0004297407,0.001430416,0.0003651323,0.007470163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006393286,"threshold_uncertainty_score":0.03381133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03431367048434421,"score_gpt":0.3036896399446429,"score_spread":0.2693759694602987,"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."}}