{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T08:23:30Z","timestamp":1778142210701,"version":"3.51.4"},"reference-count":32,"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\/501100001230","name":"Macquarie University Cyber Security Hub, in partnership with the Defence Science and Technology Group and Data61-CSIRO, through the Next Generation Technologies Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Australasian Leadership Computing Grants Scheme, with computational resources provided by NCI Australia, an NCRIS enabled capability"},{"DOI":"10.13039\/100015539","name":"Australian Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015539","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.2022.3169911","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T19:32:59Z","timestamp":1650655979000},"page":"2151-2165","source":"Crossref","is-referenced-by-count":16,"title":["A Differentially Private Framework for Deep Learning With Convexified Loss Functions"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5102-6217","authenticated-orcid":false,"given":"Zhigang","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassan Jameel","family":"Asghar","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2714-0276","authenticated-orcid":false,"given":"Mohamed Ali","family":"Kaafar","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darren","family":"Webb","sequence":"additional","affiliation":[{"name":"Cyber and Electronic Warfare Division, Defence Science and Technology Group, Edinburgh, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Dickinson","sequence":"additional","affiliation":[{"name":"Cyber and Electronic Warfare Division, Defence Science and Technology Group, Edinburgh, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1895","article-title":"Evaluating differentially private machine learning in practice","volume-title":"Proc. 28th Secur. Symp. (USENIX Secur.)","author":"Jayaraman"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref4","article-title":"ML-leaks: Model and data independent membership inference attacks and defenses on machine learning models","volume-title":"arXiv:1806.01246","author":"Salem","year":"2018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350253"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"issue":"3","key":"ref8","first-page":"1","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064047"},{"key":"ref10","first-page":"1","article-title":"Scalable private learning with pate","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Papernot"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5656-2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553453"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31346-2_50"},{"key":"ref15","first-page":"162","article-title":"Universal convexification via risk-aversion","volume-title":"Proc. 30th Conf. Uncertainty Artif. Intell.","author":"Dvijotham"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5827"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref19","volume-title":"TensorFlow Privacy","author":"Andrew","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12454"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref24","first-page":"3835","article-title":"Lipschitz regularity of deep neural networks: Analysis and efficient estimation","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Virmaux","year":"2018"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05929-w"},{"key":"ref26","volume-title":"Finding a Good Learning Rate","author":"Yedida","year":"2019"},{"key":"ref27","first-page":"123","article-title":"Convex neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bengio"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53641-4_24"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPS-ISA48467.2019.00019"},{"key":"ref32","article-title":"Data and model dependencies of membership inference attack","volume-title":"arXiv:2002.06856","author":"Tonni","year":"2020"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9652463\/09762326.pdf?arnumber=9762326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T20:53:22Z","timestamp":1705956802000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9762326\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/tifs.2022.3169911","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}