{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T23:24:42Z","timestamp":1770333882880,"version":"3.49.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tifs.2023.3266919","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T17:34:00Z","timestamp":1681407240000},"page":"2398-2412","source":"Crossref","is-referenced-by-count":20,"title":["Long-Term Privacy-Preserving Aggregation With User-Dynamics for Federated Learning"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4060-0839","authenticated-orcid":false,"given":"Ziyao","family":"Liu","sequence":"first","affiliation":[{"name":"Huawei International, Kwai Chung, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7559-9340","authenticated-orcid":false,"given":"Hsiao-Ying","family":"Lin","sequence":"additional","affiliation":[{"name":"Huawei International, Kwai Chung, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamin","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei International, Kwai Chung, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","first-page":"1","article-title":"Secure multi-party computation","volume":"78","author":"goldreich","year":"1998","journal-title":"Manuscript Preliminary Version"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7572"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref14","article-title":"MPC-enabled privacy-preserving neural network training against malicious attack","author":"liu","year":"2020","journal-title":"arXiv 2007 12557"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3460427"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"dwork","year":"2014","journal-title":"Found Trends Theor Comput Sci"},{"key":"ref10","first-page":"1","article-title":"Inverting gradients—How easy is it to break privacy in federated learning?","author":"geiping","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst 33 Annu Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3211707"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417885"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3054610"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-9709-8"},{"key":"ref50","article-title":"Secure aggregation in federated learning is not private: Leaking user data at large scale through model modification","author":"zhao","year":"2023","journal-title":"arXiv 2303 12233"},{"key":"ref46","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","author":"nguyen","year":"2022","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref45","article-title":"Secure aggregation for buffered asynchronous federated learning","author":"so","year":"2021","journal-title":"arXiv 2110 02177"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3043139"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3146448"},{"key":"ref41","first-page":"694","article-title":"LightSecAgg: A lightweight and versatile design for secure aggregation in federated learning","volume":"4","author":"so","year":"2022","journal-title":"Proc Mach Learn Syst"},{"key":"ref44","article-title":"FastSecAgg: Scalable secure aggregation for privacy-preserving federated learning","author":"kadhe","year":"2020","journal-title":"arXiv 2009 11248"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3163592"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560557"},{"key":"ref8","first-page":"5159","article-title":"Privacy-preserving Boolean range query with temporal access control in mobile computing","volume":"35","author":"tong","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3037194"},{"key":"ref9","first-page":"1","article-title":"Deep leakage from gradients","author":"zhu","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst 32 Annu Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3157603"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2022.3215986"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3088655"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"ref40","article-title":"Communication-computation efficient secure aggregation for federated learning","author":"choi","year":"2020","journal-title":"arXiv 2012 05433"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00045"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-7913-1_7"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3093711"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467210"},{"key":"ref31","first-page":"1","article-title":"The Skellam mechanism for differentially private federated learning","volume":"34","author":"agarwal","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942179"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref39","article-title":"MixNN: Protection of federated learning against inference attacks by mixing neural network layers","author":"boutet","year":"2021","journal-title":"arXiv 2109 12550"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3457388.3458665"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0012-365X(79)90084-0"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.23005"},{"key":"ref26","article-title":"Handwritten digit recognition with a back-propagation network","volume":"2","author":"lecun","year":"1989","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","author":"li","year":"2020","journal-title":"Proc 8th Int Conf Learn Represent (ICLR)"},{"key":"ref20","first-page":"1","article-title":"Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning","author":"so","year":"2021","journal-title":"Proc IACR Cryptol ePrint Arch"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0011"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24119"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref27","author":"lecun","year":"2010","journal-title":"MNIST Handwritten Digit Database"},{"key":"ref29","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10102279.pdf?arnumber=10102279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T18:22:20Z","timestamp":1684174940000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10102279\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3266919","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}