{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:08:42Z","timestamp":1764688122104,"version":"3.37.3"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3502909","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T19:06:14Z","timestamp":1732129574000},"page":"176070-176086","source":"Crossref","is-referenced-by-count":1,"title":["OptiSGD-DPWGAN: Integrating Metaheuristic Algorithms and Differential Privacy to Improve Privacy-Utility Trade-Off in Generative Models"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2667-3148","authenticated-orcid":false,"given":"Alshaymaa","family":"Ahmed Mohamed","sequence":"first","affiliation":[{"name":"Computer Science Department, College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2147-8017","authenticated-orcid":false,"given":"Yasmine N. M.","family":"Saleh","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9953-7147","authenticated-orcid":false,"given":"Ayman A.","family":"Abdel-Hamid","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/nejmra1814259"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737494"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155359"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.02.001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.48"},{"key":"ref10","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.4135\/9781412983907.n1717"},{"key":"ref12","first-page":"2545","article-title":"Variants of RMSProp and Adagrad with logarithmic regret bounds","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Mukkamala"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"volume-title":"Apple\u2019s \u2019Differential Privacy\u2019 Is About Collecting Your Data-But Not Your Data","year":"2016","author":"Greenberg","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref16","article-title":"Differentially private generative adversarial network","author":"Xie","year":"2018","journal-title":"arXiv:1802.06739"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2997604"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref19","first-page":"17","article-title":"Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing","volume-title":"Proc. 23rd Secur. Symp. (USENIX Security)","author":"Fredrikson"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947295"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00019"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220076"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0008"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.86"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-59140-557-3.ch189"},{"key":"ref30","first-page":"2465","article-title":"Learning rate adaptation for differentially private learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Koskela"},{"key":"ref31","first-page":"4517","article-title":"Partially encrypted deep learning using functional encryption","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ryffel"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-92058-0_74"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00204"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.05.005"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/DSC.2019.00105"},{"key":"ref41","article-title":"Generating synthetic but plausible healthcare record datasets","author":"Avi\u00f1\u00f3","year":"2018","journal-title":"arXiv:1807.01514"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00353-9"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231757"},{"article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Radford","key":"ref44"},{"key":"ref45","first-page":"1","article-title":"CorGAN: Correlation-capturing convolutional generative adversarial networks for generating synthetic healthcare records","volume-title":"Proc. 33rd Int. Florida Artif. Intell. Res. Soc. Conf. (FLAIRS)","author":"Torfi"},{"key":"ref46","article-title":"R\u00e9nyi differential privacy of the sampled Gaussian mechanism","author":"Mironov","year":"2019","journal-title":"arXiv:1908.10530"},{"key":"ref47","first-page":"5767","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Gulrajani"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/kr.2021\/24"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/jsait.2020.2983071"},{"key":"ref50","first-page":"7335","article-title":"Modeling tabular data using conditional GAN","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Xu"},{"key":"ref51","first-page":"1","article-title":"PATE-GAN: Generating synthetic data with differential privacy guarantees","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Jordon"},{"key":"ref52","first-page":"1","article-title":"Semi-supervised knowledge transfer for deep learning from private training data","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Papernot"},{"key":"ref53","first-page":"1","article-title":"A survey of differentially private generative adversarial networks","volume-title":"Proc. AAAI Workshop Privacy-Preserving Artif. Intell. (PPAI)","author":"Fan"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.12"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.723"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"ref57","first-page":"214","article-title":"Wasserstein GAN","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Arjovsky"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx079"},{"key":"ref59","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst. (NIPS)","author":"Paszke"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3129612"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.81"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.pneurobio.2011.09.005"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188395"},{"key":"ref66","first-page":"1","article-title":"Empirical evaluation of amplifying privacy by subsampling for GANs to create differentially private synthetic tabular data","volume-title":"Proc. TKTP","author":"Nieminen"},{"key":"ref67","article-title":"Differentially private synthetic data: Applied evaluations and enhancements","author":"Rosenblatt","year":"2020","journal-title":"arXiv:2011.05537"},{"key":"ref68","article-title":"PATE-TripleGAN: Privacy-preserving image synthesis with Gaussian differential privacy","author":"Jiang","year":"2024","journal-title":"arXiv:2404.12730"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10758629.pdf?arnumber=10758629","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T07:32:37Z","timestamp":1733297557000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10758629\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3502909","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}