{"id":"W4403826461","doi":"10.1109/comst.2024.3486690","title":"Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Information and Communications Technology; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Information privacy; Data science; Internet privacy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006159516,0.001567082,0.002279734,0.001479474,0.0006901418,0.004251772,0.002933699,0.0023979,0.001913701],"category_scores_gemma":[0.01190787,0.0008404568,0.002342327,0.002918303,0.001835735,0.006596649,0.002754489,0.004409726,0.0007629705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002435315,"about_ca_system_score_gemma":0.002676327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002484289,"about_ca_topic_score_gemma":0.001387575,"domain_scores_codex":[0.9959415,0.0015453,0.0003512801,0.0007469385,0.001175465,0.0002394508],"domain_scores_gemma":[0.9908517,0.006823503,0.0004743291,0.0009236567,0.0007851362,0.0001416924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001777376,0.0002473574,0.003351201,0.001771866,0.0003437987,0.0002987925,0.0003708785,0.3047885,0.0008448454,0.3957341,0.01230776,0.2797632],"study_design_scores_gemma":[0.00002112,0.0002013386,0.000656023,0.0005929379,0.0001073096,0.0004621701,0.0001184084,0.6866954,0.0008530813,0.2718066,0.03841449,0.00007108547],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006575169,0.08139158,0.8979562,0.005104598,0.0003983981,0.0001196079,0.0006063235,0.0002976838,0.007550433],"genre_scores_gemma":[0.5310099,0.2680313,0.182522,0.003411956,0.00299479,0.0006779601,0.00219421,0.0003252678,0.008832711],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006159516,"threshold_uncertainty_score":0.03257501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2203521783042907,"score_gpt":0.3738232961601144,"score_spread":0.1534711178558237,"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."}}