{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:02:49Z","timestamp":1779926569741,"version":"3.53.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023QF133"],"award-info":[{"award-number":["ZR2023QF133"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["2025TSGCZZB0016"],"award-info":[{"award-number":["2025TSGCZZB0016"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["2024ZD011"],"award-info":[{"award-number":["2024ZD011"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12201356"],"award-info":[{"award-number":["12201356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472265"],"award-info":[{"award-number":["62472265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472266"],"award-info":[{"award-number":["62472266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302280"],"award-info":[{"award-number":["62302280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U25B2031"],"award-info":[{"award-number":["U25B2031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Standards & Interfaces"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.csi.2026.104160","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T07:29:25Z","timestamp":1773646165000},"page":"104160","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MKXGB: Privacy-preserving XGBoost training over encrypted multi-source data via multi-key homomorphic encryption"],"prefix":"10.1016","volume":"98","author":[{"given":"Huiwen","family":"Lv","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3472-3699","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3386-4756","authenticated-orcid":false,"given":"Lijuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4912-3197","authenticated-orcid":false,"given":"Ye","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.csi.2026.104160_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103120","article-title":"GenRAN: GenFusion-guided reversible anonymization network for face privacy preserving","volume":"121","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"issue":"3","key":"10.1016\/j.csi.2026.104160_b2","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1108\/FS-01-2023-0016","article-title":"Peanut oil price change forecasts through the neural network","volume":"27","author":"Jin","year":"2025","journal-title":"Foresight"},{"issue":"3","key":"10.1016\/j.csi.2026.104160_b3","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1504\/IJMME.2024.140697","article-title":"Regional steel price index predictions for North China through machine learning","volume":"15","author":"Jin","year":"2024","journal-title":"Int. J. Min. Min. Eng."},{"key":"10.1016\/j.csi.2026.104160_b4","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.csi.2026.104160_b5","doi-asserted-by":"crossref","unstructured":"X. He, J. Pan, O. Jin, T. Xu, B. Liu, et al., Practical Lessons from Predicting Clicks on Ads at Facebook, in: Proc. 8th Int. Workshop Data Min. Online Advert., 2014, pp. 1\u20139.","DOI":"10.1145\/2648584.2648589"},{"issue":"1","key":"10.1016\/j.csi.2026.104160_b6","first-page":"99","article-title":"Detecting click fraud in online advertising: A data mining approach","volume":"15","author":"Oentaryo","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.csi.2026.104160_b7","doi-asserted-by":"crossref","unstructured":"X. Ling, W. Deng, C. Gu, H. Zhou, C. Li, F. Sun, Model Ensemble for Click Prediction in Bing Search Ads, in: Proc. 26th Int. Conf. World Wide Web Companion, WWW, 2017, pp. 689\u2013698.","DOI":"10.1145\/3041021.3054192"},{"key":"10.1016\/j.csi.2026.104160_b8","series-title":"Regulation (EU) 2016\/679, general data protection regulation","author":"European Parliament and Council","year":"2016"},{"key":"10.1016\/j.csi.2026.104160_b9","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A.y. Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, in: Proc. 20th Int. Conf. Artif. Intell. Stat., AISTATS, 2017, pp. 1273\u20131282."},{"key":"10.1016\/j.csi.2026.104160_b10","series-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","first-page":"4642","article-title":"Practical federated gradient boosting decision trees","volume":"vol. 34","author":"Li","year":"2020"},{"key":"10.1016\/j.csi.2026.104160_b11","doi-asserted-by":"crossref","unstructured":"Y. Liu, Z. Ma, X. Liu, S. Ma, S. Nepal, R.H. Deng, K. Ren, Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing, in: Proc. IEEE 40th Int. Conf. Distrib. Comput. Syst., ICDCS, 2020, pp. 1\u201311.","DOI":"10.1109\/ICDCS47774.2020.00017"},{"issue":"6","key":"10.1016\/j.csi.2026.104160_b12","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","article-title":"SecureBoost: A lossless federated learning framework","volume":"36","author":"Cheng","year":"2021","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.csi.2026.104160_b13","series-title":"International Conference on the Theory and Applications of Cryptographic Techniques","first-page":"223","article-title":"Public-key cryptosystems based on composite degree residuosity classes","author":"Paillier","year":"1999"},{"key":"10.1016\/j.csi.2026.104160_b14","doi-asserted-by":"crossref","DOI":"10.1109\/TSC.2024.3394706","article-title":"ELXGB: An efficient and privacy-preserving xgboost for vertical federated learning","author":"Xu","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"10.1016\/j.csi.2026.104160_b15","doi-asserted-by":"crossref","unstructured":"M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 308\u2013318.","DOI":"10.1145\/2976749.2978318"},{"key":"10.1016\/j.csi.2026.104160_b16","series-title":"2019 IEEE 35th International Conference on Data Engineering","first-page":"638","article-title":"Collecting and analyzing multidimensional data with local differential privacy","author":"Wang","year":"2019"},{"key":"10.1016\/j.csi.2026.104160_b17","unstructured":"C. Jin, J. Wang, S.G. Teo, L. Zhang, C. Chan, Q. Hou, K.M.M. Aung, Towards end-to-end secure and efficient federated learning for xgboost, in: Proceedings of the AAAI International Workshop on Trustable, Verifiable and Auditable Federated Learning, 2022."},{"issue":"1","key":"10.1016\/j.csi.2026.104160_b18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3709723","article-title":"SecureXGB: A secure and efficient multi-party protocol for vertical federated XGBoost","volume":"3","author":"Han","year":"2025","journal-title":"Proc. ACM Manag. Data"},{"key":"10.1016\/j.csi.2026.104160_b19","series-title":"International Conference on Coding and Cryptology","first-page":"11","article-title":"Secret-sharing schemes: A survey","author":"Beimel","year":"2011"},{"key":"10.1016\/j.csi.2026.104160_b20","series-title":"Theory of Cryptography","first-page":"253","article-title":"Functional encryption: Definitions and challenges","author":"Boneh","year":"2011"},{"key":"10.1016\/j.csi.2026.104160_b21","article-title":"FederBoost: Private federated learning for GBDT","author":"Tian","year":"2023","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.csi.2026.104160_b22","doi-asserted-by":"crossref","unstructured":"H. Cai, J. Cai, L. Sun, An adaptive gradient privacy-preserving algorithm for federated XGBoost, in: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning, 2023, pp. 277\u2013282.","DOI":"10.1145\/3590003.3590051"},{"issue":"4","key":"10.1016\/j.csi.2026.104160_b23","doi-asserted-by":"crossref","DOI":"10.3934\/era.2024130","article-title":"A differentially private distributed collaborative xgboost method.","volume":"32","author":"Li","year":"2024","journal-title":"Electron. Res. Arch."},{"key":"10.1016\/j.csi.2026.104160_b24","series-title":"2019 IEEE International Conference on Big Data","first-page":"1312","article-title":"Securegbm: Secure multi-party gradient boosting","author":"Feng","year":"2019"},{"key":"10.1016\/j.csi.2026.104160_b25","doi-asserted-by":"crossref","unstructured":"W. Fang, D. Zhao, J. Tan, C. Chen, C. Yu, L. Wang, L. Wang, J. Zhou, B. Zhang, Large-scale secure XGB for vertical federated learning, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 443\u2013452.","DOI":"10.1145\/3459637.3482361"},{"issue":"5","key":"10.1016\/j.csi.2026.104160_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3523061","article-title":"An efficient learning framework for federated XGBoost using secret sharing and distributed optimization","volume":"13","author":"Xie","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"10.1016\/j.csi.2026.104160_b27","doi-asserted-by":"crossref","unstructured":"W. Dai, H. Jin, D. Zou, S. Xu, W. Zheng, L. Shi, TEE: A virtual DRTM based execution environment for secure cloud-end computing, in: Proceedings of the 17th ACM Conference on Computer and Communications Security, 2010, pp. 663\u2013665.","DOI":"10.1145\/1866307.1866390"},{"issue":"6","key":"10.1016\/j.csi.2026.104160_b28","doi-asserted-by":"crossref","first-page":"5755","DOI":"10.1109\/TDSC.2024.3384244","article-title":"Privacy-preserving collaborative learning for genome analysis via secure XGBoost","volume":"21","author":"Aldeen","year":"2024","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"issue":"5","key":"10.1016\/j.csi.2026.104160_b29","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TAI.2021.3139055","article-title":"PIVODL: Privacy-preserving vertical federated learning over distributed labels","volume":"4","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.csi.2026.104160_b30","series-title":"Secureboost+: A high performance gradient boosting tree framework for large scale vertical federated learning","first-page":"arXiv","author":"Chen","year":"2021"},{"key":"10.1016\/j.csi.2026.104160_b31","series-title":"Efficient batch homomorphic encryption for vertically federated xgboost","author":"Xu","year":"2021"},{"key":"10.1016\/j.csi.2026.104160_b32","series-title":"31st USENIX Security Symposium","first-page":"809","article-title":"Cheetah: Lean and fast secure two-party deep neural network inference","author":"Huang","year":"2022"},{"key":"10.1016\/j.csi.2026.104160_b33","series-title":"ICC 2023-IEEE International Conference on Communications","first-page":"1566","article-title":"Toward efficient and end-to-end privacy-preserving distributed gradient boosting decision trees","author":"Yuan","year":"2023"},{"key":"10.1016\/j.csi.2026.104160_b34","doi-asserted-by":"crossref","unstructured":"A. L\u00f3pez-Alt, E. Tromer, V. Vaikuntanathan, On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption, in: Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing, 2012, pp. 1219\u20131234.","DOI":"10.1145\/2213977.2214086"},{"key":"10.1016\/j.csi.2026.104160_b35","doi-asserted-by":"crossref","unstructured":"H. Chen, W. Dai, M. Kim, Y. Song, Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, pp. 395\u2013412.","DOI":"10.1145\/3319535.3363207"},{"key":"10.1016\/j.csi.2026.104160_b36","doi-asserted-by":"crossref","unstructured":"T. Kim, H. Kwak, D. Lee, J. Seo, Y. Song, Asymptotically faster multi-key homomorphic encryption from homomorphic gadget decomposition, in: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023, pp. 726\u2013740.","DOI":"10.1145\/3576915.3623176"},{"key":"10.1016\/j.csi.2026.104160_b37","series-title":"European Symposium on Research in Computer Security","first-page":"217","article-title":"Fully homomorphic training and inference on binary decision tree and random forest","author":"Shin","year":"2024"},{"key":"10.1016\/j.csi.2026.104160_b38","series-title":"Advances in Cryptology \u2013 ASIACRYPT 2020","first-page":"221","article-title":"Efficient homomorphic comparison methods with optimal complexity","author":"Cheon","year":"2020"},{"key":"10.1016\/j.csi.2026.104160_b39","series-title":"Efficient Secure Two-Party Protocols: Techniques and Constructions","author":"Hazay","year":"2010"},{"key":"10.1016\/j.csi.2026.104160_b40","series-title":"The UCI machine learning repository","author":"Kelly","year":"2023"},{"key":"10.1016\/j.csi.2026.104160_b41","series-title":"Sepsis survival minimal clinical records","author":"Chicco","year":"2023"},{"issue":"3","key":"10.1016\/j.csi.2026.104160_b42","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1093\/biomet\/66.3.605","article-title":"On optimal and data-based histograms","volume":"66","author":"Scott","year":"1979","journal-title":"Biometrika"}],"container-title":["Computer Standards & Interfaces"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000346?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000346?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:08:47Z","timestamp":1779923327000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920548926000346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":42,"alternative-id":["S0920548926000346"],"URL":"https:\/\/doi.org\/10.1016\/j.csi.2026.104160","relation":{},"ISSN":["0920-5489"],"issn-type":[{"value":"0920-5489","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MKXGB: Privacy-preserving XGBoost training over encrypted multi-source data via multi-key homomorphic encryption","name":"articletitle","label":"Article Title"},{"value":"Computer Standards & Interfaces","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csi.2026.104160","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104160"}}