{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001358053,0.0006644534,0.0007500796,0.0007457071,0.0007590348,0.0008070596,0.003626922,0.0003938747,0.00001766223],"category_scores_gemma":[0.0001492097,0.0007255133,0.0002060605,0.001245488,0.00006894214,0.001382889,0.0002219759,0.001129764,0.00002948229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296267,"about_ca_system_score_gemma":0.000266542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000136194,"about_ca_topic_score_gemma":0.0001251789,"domain_scores_codex":[0.9956354,0.00008164571,0.0009322669,0.002081962,0.0002501061,0.001018633],"domain_scores_gemma":[0.9956218,0.001213717,0.0001552612,0.002592576,0.0001498973,0.0002667637],"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":[0.00007802737,0.0007030299,0.00001429348,0.001376263,0.001794274,0.0000262236,0.003828216,0.2790382,0.0002186151,0.04716965,0.002672945,0.6630803],"study_design_scores_gemma":[0.0006344202,0.00008707184,0.000007590589,0.00222593,0.0004143286,0.000008556223,0.0001968669,0.9832602,0.0002865819,0.0000257691,0.01220851,0.0006442092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001309146,0.004777446,0.9874578,0.0002643457,0.004459931,0.0004770357,0.000833816,0.0002588228,0.0001616331],"genre_scores_gemma":[0.9423321,0.0007142004,0.05581179,0.0001451117,0.0004029953,0.00003851007,0.000224783,0.00005971494,0.0002708328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9410229,"threshold_uncertainty_score":0.9995196,"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."}}