{"id":"W4390447397","doi":"10.1007/978-3-031-50959-9_42","title":"Preventing Text Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Backdoor; Computer science; Encryption; Scheme (mathematics); Computer security; Vulnerability (computing); Server; Federated learning; Key (lock); Enhanced Data Rates for GSM Evolution; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001955802,0.0004196469,0.001017342,0.0007010935,0.0008243349,0.002061701,0.001359295,0.001564817,0.00253221],"category_scores_gemma":[0.006129776,0.0004231439,0.0008162242,0.0008713188,0.00125302,0.005228314,0.003093548,0.001693806,0.001241726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009780863,"about_ca_system_score_gemma":0.001273256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003201197,"about_ca_topic_score_gemma":0.0002398833,"domain_scores_codex":[0.9976513,0.0005733702,0.0002135827,0.0003977026,0.0007879837,0.0003759932],"domain_scores_gemma":[0.9956719,0.001460011,0.0003460043,0.001993663,0.0004381449,0.00009042121],"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":[0.002706726,0.0005353949,0.002726642,0.0004906753,0.0002560078,0.00103272,0.0006656885,0.1466108,0.05845482,0.3138387,0.01350891,0.4591728],"study_design_scores_gemma":[0.00006724894,0.0002220641,0.0005187319,0.00006858721,0.00007768036,0.001044353,0.00009873195,0.7903938,0.05799393,0.1429046,0.006563689,0.00004654175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08250766,0.0005702656,0.9078579,0.0006391361,0.0001848966,0.0001597063,0.0001523046,0.00208371,0.005844472],"genre_scores_gemma":[0.8921227,0.0002226404,0.1000043,0.0001787611,0.00006836677,0.00007332928,0.0001540715,0.00009131149,0.007084642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00253221,"threshold_uncertainty_score":0.01034343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04871231188539649,"score_gpt":0.2943537465823932,"score_spread":0.2456414346969967,"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."}}