{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:15:25Z","timestamp":1740100525729,"version":"3.37.3"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072359,62072352"],"award-info":[{"award-number":["62072359,62072352"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1109\/globecom46510.2021.9686029","type":"proceedings-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T21:59:04Z","timestamp":1643839144000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["FedGR: A Lossless-Obfuscation Approach for Secure Federated Learning"],"prefix":"10.1109","author":[{"given":"Wenjing","family":"Qin","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"aono","year":"2017","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"ref11","article-title":"Secure federated matrix factorization","author":"chai","year":"2020","journal-title":"IEEE Intelligent Systems"},{"key":"ref12","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_14"},{"key":"ref14","first-page":"223","article-title":"Public-key cryptosystems based on composite degree residu-osity classes","author":"paillier","year":"0","journal-title":"International Conference on the Theory and Applications of Cryptographic Techniques"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref16"},{"key":"ref17","article-title":"Labeled faces in the wild: A database forstudying face recognition in unconstrained environments","author":"huang","year":"0","journal-title":"Workshop on Faces in 'Real-Life' Images Detection Alignment and Recognition"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/359168.359176"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1986.25"},{"key":"ref8","first-page":"1895","article-title":"Evaluating differentially private ma-chine learning in practice","author":"jayaraman","year":"0","journal-title":"28th USENIX Security Symposium ( USENIX Security 19)"},{"key":"ref7","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-030-63076-8_2","article-title":"Deep leakage from gradients","author":"zhu","year":"2020","journal-title":"Federated Learning"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"}],"event":{"name":"GLOBECOM 2021 - 2021 IEEE Global Communications Conference","start":{"date-parts":[[2021,12,7]]},"location":"Madrid, Spain","end":{"date-parts":[[2021,12,11]]}},"container-title":["2021 IEEE Global Communications Conference (GLOBECOM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9685019\/9685006\/09686029.pdf?arnumber=9686029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:25:31Z","timestamp":1654547131000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9686029\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/globecom46510.2021.9686029","relation":{},"subject":[],"published":{"date-parts":[[2021,12]]}}}