{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T09:29:42Z","timestamp":1779355782864,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["61876221"],"award-info":[{"award-number":["61876221"]}],"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":["61876220"],"award-info":[{"award-number":["61876220"]}],"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":["61976164"],"award-info":[{"award-number":["61976164"]}],"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":["62072334"],"award-info":[{"award-number":["62072334"]}],"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":["61836009"],"award-info":[{"award-number":["61836009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi","doi-asserted-by":"publisher","award":["2022GY-061"],"award-info":[{"award-number":["2022GY-061"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tifs.2022.3170271","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T20:40:31Z","timestamp":1650919231000},"page":"1814-1826","source":"Crossref","is-referenced-by-count":7,"title":["Laplacian Smoothing Stochastic ADMMs With Differential Privacy Guarantees"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-8533","authenticated-orcid":false,"given":"Yuanyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5629-893X","authenticated-orcid":false,"given":"Jiacheng","family":"Geng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1040-352X","authenticated-orcid":false,"given":"Fanhua","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-2851","authenticated-orcid":false,"given":"Weixin","family":"An","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-2749","authenticated-orcid":false,"given":"Hongying","family":"Liu","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3809-1086","authenticated-orcid":false,"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064047"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/548"},{"key":"ref4","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref5","first-page":"1","article-title":"Private convex empirical risk minimization and high-dimensional regression","volume-title":"Proc. Int. Conf. Learn. Theory","author":"Kifer"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00001"},{"key":"ref7","article-title":"Gradient perturbation is underrated for differentially private convex optimization","volume-title":"arXiv:1911.11363","author":"Yu","year":"2019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref9","first-page":"2719","article-title":"Differentially private empirical risk minimization revisited: Faster and more general","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Wang"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220076"},{"key":"ref11","first-page":"328","article-title":"DP-LSSGD: A stochastic optimization method to lift the utility in privacy-preserving ERM","volume-title":"Proc. 1st Conf. Math. Sci. Mach. Learn.","author":"Wang"},{"key":"ref12","article-title":"Differentially private federated learning with Laplacian smoothing","volume-title":"arXiv:2005.00218","author":"Liang","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp218"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1214\/11-aos878"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3374664.3375733"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1137\/120896219"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-016-1034-2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1090\/mcom\/3388"},{"key":"ref19","first-page":"46","article-title":"Fast stochastic alternating direction method of multipliers","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Zhong"},{"key":"ref20","first-page":"736","article-title":"Stochastic dual coordinate ascent with alternating direction method of multipliers","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Suzuki"},{"key":"ref21","first-page":"2407","article-title":"Fast-and-light stochastic ADMM","volume-title":"Proc. 25th Int. Joint Conf. Artif. Intell.","author":"Zheng"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3000512"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10843"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.09.020"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005716"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2947867"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s40687-022-00351-1"},{"key":"ref28","first-page":"80","article-title":"Stochastic alternating direction method of multipliers","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Ouyang"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.v7i3.405"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref32","article-title":"Efficient privacy-preserving nonconvex optimization","volume-title":"arXiv:1910.13659","author":"Wang","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1561\/9781601984616"},{"key":"ref34","first-page":"505","article-title":"Linear time solver for primal SVM","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Nie"},{"key":"ref35","first-page":"1119","article-title":"Online alternating direction method","volume-title":"Proc. 29th Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref36","first-page":"392","article-title":"Dual averaging and proximal gradient descent for online alternating direction multiplier method","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Suzuki"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2878765"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3113768"},{"key":"ref39","first-page":"4479","article-title":"Faster and non-ergodic O(1\/K) stochastic alternating direction method of multipliers","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Li"},{"key":"ref40","first-page":"1267","article-title":"ADMM without a fixed penalty parameter: Faster convergence with new adaptive penalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref41","first-page":"315","article-title":"Accelerating stochastic gradient descent using predictive variance reduction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Johnson"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICTCS.2017.60"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1137\/110836936"},{"key":"ref44","first-page":"485","article-title":"Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data","volume":"9","author":"Banerjee","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070539"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9652463\/09762729.pdf?arnumber=9762729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T21:02:57Z","timestamp":1705957377000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9762729\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/tifs.2022.3170271","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}