{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:24:53Z","timestamp":1775420693622,"version":"3.50.1"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0101100"],"award-info":[{"award-number":["2018AAA0101100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Intell. Syst."],"published-print":{"date-parts":[[2021,11,1]]},"DOI":"10.1109\/mis.2021.3082561","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T19:48:05Z","timestamp":1621972085000},"page":"87-98","source":"Crossref","is-referenced-by-count":452,"title":["SecureBoost: A Lossless Federated Learning Framework"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2139-3451","authenticated-orcid":false,"given":"Kewei","family":"Cheng","sequence":"first","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, USA"}]},{"given":"Tao","family":"Fan","sequence":"additional","affiliation":[{"name":"WeBank, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-7622","authenticated-orcid":false,"given":"Yilun","family":"Jin","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3800-3533","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"WeBank, Shenzhen, China"}]},{"given":"Tianjian","family":"Chen","sequence":"additional","affiliation":[{"name":"WeBank, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-3015","authenticated-orcid":false,"given":"Dimitrios","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"WeBank, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66402-6_8"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref12","author":"yang","year":"2019","journal-title":"Federated Learning"},{"key":"ref13","article-title":"A communication efficient vertical federated learning framework","author":"liu","year":"2019"},{"key":"ref14","article-title":"Self-supervised cross-silo federated neural architecture search","author":"liang","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-70992-5_14"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/11535706_11"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486352"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"ref28","article-title":"Privacy-preserving entity resolution and logistic regression on encrypted data","author":"djatmiko","year":"2017","journal-title":"Private and Secure Machine Learning (PSML)"},{"key":"ref3","first-page":"315","article-title":"Regime change: Enabling big data through Europe’s new data protection regulation","volume":"17","author":"mayer-schonberger","year":"2015","journal-title":"Columbia Sci Technol Law Rev"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/11535706_11"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref5","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"hardy","year":"2017"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25952-7_6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.21552\/EDPL\/2016\/3\/4"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvr7fcwb"},{"key":"ref9","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5895"},{"key":"ref22","article-title":"Lower bounds and optimal algorithms for personalized federated learning","author":"hanzely","year":"2020"},{"key":"ref21","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"2018"},{"key":"ref24","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","author":"yurochkin","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","first-page":"4615","article-title":"Agnostic federated learning","author":"mohri","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988525"},{"key":"ref25","article-title":"Federated learning with matched averaging","author":"wang","year":"2020"}],"container-title":["IEEE Intelligent Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9670\/9658176\/09440789.pdf?arnumber=9440789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:51:20Z","timestamp":1652194280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9440789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":33,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/mis.2021.3082561","relation":{},"ISSN":["1541-1672","1941-1294"],"issn-type":[{"value":"1541-1672","type":"print"},{"value":"1941-1294","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,1]]}}}