{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T05:01:15Z","timestamp":1780117275058,"version":"3.54.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Yunnan Provincial Archives Bureau Science and Technology","award":["81"],"award-info":[{"award-number":["81"]}]},{"name":"Science Research Fund Project of Yunnan Provincial Department of Education","award":["2024J1690"],"award-info":[{"award-number":["2024J1690"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1109\/tetci.2026.3670862","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:58:24Z","timestamp":1774468704000},"page":"2456-2470","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Preserving Against Gradients Leakage Attacks via Joint Differential Privacy in Federated Learning"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3492-3790","authenticated-orcid":false,"given":"Yu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering (School of Cybersecurity), University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-1450","authenticated-orcid":false,"given":"Jianping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cybersecurity), University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5911-5805","authenticated-orcid":false,"given":"Xunyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110638"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3210597"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119784"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111519"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2022.3200724"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120295"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3081560"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3341299"},{"key":"ref9","article-title":"Fed-GLOSS-DP: Federated, global learning using synthetic sets with record level differential privacy","author":"Wang","year":"2023"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3670351"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681966"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179466"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103744"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3377655"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3317870"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3303718"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3293417"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3208736"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.56553\/popets-2025-0023"},{"key":"ref20","article-title":"Secure distributed learning for CAVs: Defending against gradient leakage with leveled homomorphic encryption","author":"Najjar","year":"2025"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00473"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447749"},{"key":"ref24","article-title":"Tensorflow privacy repository V0.14.0.","year":"2025"},{"key":"ref25","first-page":"1945","article-title":"Personalization improves privacy-accuracy tradeoffs in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bietti","year":"2022"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128349"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2022-0043"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3131258"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00458"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0173"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3510032"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00021"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01015"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26163"},{"key":"ref37","article-title":"Learning across data owners with joint differential privacy","author":"Huang","year":"2023"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/sp54263.2024.00134"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3515793"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2013.6785787"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/LICS52264.2021.9470718"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3306159"},{"key":"ref45","article-title":"The Gaussian mixing mechanism: Renyi differential privacy via Gaussian sketches","author":"Lev","year":"2025"},{"key":"ref46","first-page":"29181","article-title":"Renyi differential privacy of the subsampled shuffle model in distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Girgis","year":"2021"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3140131"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/2554797.2554834"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3423657"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-ijcnlp.7"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/2590296.2590320"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560561"},{"key":"ref53","first-page":"13931","article-title":"Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Singh","year":"2023"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00988"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3244092"},{"key":"ref57","first-page":"3533","article-title":"Do adversarially robust imagenet models transfer better","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Salman","year":"2020"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00078"},{"key":"ref59","first-page":"1","article-title":"Enhancing adversarial defense by k-winners-take-all","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xiao","year":"2020"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7433297\/11538055\/11456084.pdf?arnumber=11456084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T04:17:24Z","timestamp":1780114644000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11456084\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":59,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2026.3670862","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]}}}