{"id":"W4313547868","doi":"10.1109/tifs.2022.3232955","title":"SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Boosting (machine learning); Raw data; Federated learning; Tree (set theory); Information privacy; Data mining; Machine learning; Artificial intelligence; Computer security","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004603125,0.0001823712,0.0001712975,0.0002217706,0.001347208,0.000499117,0.002817328,0.000114112,0.00002381464],"category_scores_gemma":[0.000673447,0.0001902988,0.0000388482,0.0004789693,0.0001138348,0.001593965,0.001553026,0.0007464545,0.000004717599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009106928,"about_ca_system_score_gemma":0.00004992475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005188345,"about_ca_topic_score_gemma":0.00001344627,"domain_scores_codex":[0.9984654,0.00008587982,0.0003775364,0.0003076942,0.0004492346,0.0003142997],"domain_scores_gemma":[0.9976614,0.0002145156,0.00008588981,0.001814886,0.0000907022,0.0001326665],"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":[0.000443552,0.00096733,0.0005402925,0.0003928097,0.0002497101,0.00004432091,0.03036032,0.03881781,0.0003931707,0.1307214,0.01845798,0.7786113],"study_design_scores_gemma":[0.0003430591,0.0002054851,0.0002549676,0.00002140699,0.000009631894,0.00004500364,0.0004296859,0.9229603,0.001282678,0.07333944,0.0008829467,0.0002253923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2292101,0.00002301282,0.7660686,0.003409473,0.0003653108,0.000207405,0.00005766449,0.000421001,0.0002374556],"genre_scores_gemma":[0.9524943,0.00002186648,0.04691666,0.0004906071,0.000009785012,0.00004019482,0.00001703784,0.000007713638,0.000001833032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8841425,"threshold_uncertainty_score":0.9999529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755115270507255,"score_gpt":0.2482139234666468,"score_spread":0.2306627707615742,"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."}}