{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T02:56:44Z","timestamp":1774753004280,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation"},{"name":"Korean Government through Ministry of Science and ICT (MSIT)","award":["2020-0-00952"],"award-info":[{"award-number":["2020-0-00952"]}]},{"DOI":"10.13039\/501100003725","name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2019R1A2C1003146"],"award-info":[{"award-number":["2019R1A2C1003146"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3137278","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T20:55:15Z","timestamp":1640120115000},"page":"167412-167425","source":"Crossref","is-referenced-by-count":11,"title":["Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack"],"prefix":"10.1109","volume":"9","author":[{"given":"Cheolhee","family":"Park","sequence":"first","affiliation":[]},{"given":"Youngsoo","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4973-7700","authenticated-orcid":false,"given":"Jong-Geun","family":"Park","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9690-5055","authenticated-orcid":false,"given":"Dowon","family":"Hong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0779-3539","authenticated-orcid":false,"given":"Changho","family":"Seo","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5656-2"},{"key":"ref38","first-page":"1309","article-title":"Differential privacy preservation for deep auto-encoders: An application of human behavior prediction","author":"phan","year":"2016","journal-title":"Proc 30th AAAI Conf Artif Intell"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835868"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref31","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"chaudhuri","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350253"},{"key":"ref35","first-page":"289","article-title":"Privacy-preserving logistic regression","author":"chaudhuri","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682219"},{"key":"ref28","article-title":"Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers","author":"ateniese","year":"2013","journal-title":"arXiv 1306 4447"},{"key":"ref27","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"karras","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2011.40"},{"key":"ref2","first-page":"2773","article-title":"Grammar as a foreign language","author":"vinyals","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2938759"},{"key":"ref22","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref21","first-page":"1","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","first-page":"2180","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref23","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref26","article-title":"BEGAN: Boundary equilibrium generative adversarial networks","author":"berthelot","year":"2017","journal-title":"arXiv 1703 10717"},{"key":"ref25","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","first-page":"1","article-title":"GS-WGAN: A gradient-sanitized approach for learning differentially private generators","author":"chen","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref51","article-title":"RDP-GAN: A Rényi-differential privacy based generative adversarial network","author":"ma","year":"2020","journal-title":"arXiv 2007 02056"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref54","article-title":"Quality labeled faces in the wild (QLFW): A database for studying face recognition in real-world environments","volume":"9394","author":"karam","year":"2015","journal-title":"Proc SPIE Human Vis Electron Imag"},{"key":"ref53","author":"andrew","year":"2021","journal-title":"Tensorflow Privacy"},{"key":"ref52","article-title":"Rényi differential privacy of the sampled Gaussian mechanism","author":"mironov","year":"2019","journal-title":"arXiv 1908 10530"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417238"},{"key":"ref11","first-page":"265","article-title":"Calibrating noise to sensitivity in private data analysis","author":"dwork","year":"2006","journal-title":"Proc Theory Cryptogr Conf"},{"key":"ref40","article-title":"Differentially private generative adversarial network","author":"xie","year":"2018","journal-title":"arXiv 1802 06739"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref13","article-title":"Concentrated differential privacy","author":"dwork","year":"2016","journal-title":"arXiv 1603 01887"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53641-4_24"},{"key":"ref15","first-page":"263","article-title":"Rényi differential privacy","author":"mironov","year":"2017","journal-title":"Proc IEEE 30th Comput Secur Found Symp (CSF)"},{"key":"ref16","first-page":"61","article-title":"Membership inference attack against differentially private deep learning model","volume":"11","author":"rahman","year":"2018","journal-title":"Trans Data Privacy"},{"key":"ref17","first-page":"1003","article-title":"Regression model fitting under differential privacy and model inversion attack","author":"wang","year":"2015","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref18","article-title":"Privacy-preserving machine learning through data obfuscation","author":"zhang","year":"2018","journal-title":"arXiv 1807 01860"},{"key":"ref19","first-page":"1895","article-title":"Evaluating differentially private machine learning in practice","author":"jayaraman","year":"2019","journal-title":"Proc 28th USENIX Secur Symp"},{"key":"ref4","first-page":"17","article-title":"Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing","author":"fredrikson","year":"2014","journal-title":"Proc 23rd USENIX Secur Symp"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0008"},{"key":"ref7","first-page":"601","article-title":"Stealing machine learning models via prediction APIs","author":"tram\u00e8r","year":"2016","journal-title":"Proc 25th USENIX Secur Symp"},{"key":"ref49","first-page":"1","article-title":"PATE-GAN: Generating synthetic data with differential privacy guarantees","author":"jordon","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0067"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22312-0_11"},{"key":"ref48","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.118.005122"},{"key":"ref42","first-page":"5767","article-title":"Improved training of wasserstein gans","author":"gulrajani","year":"2017","journal-title":"Proc Annu Conf Neural Inf Process Syst"},{"key":"ref41","author":"alzantot","year":"2019","journal-title":"Differential Privacy Synthetic Data Generation Using WGANs"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2897874"},{"key":"ref43","article-title":"Differentially private releasing via deep generative model (technical report)","author":"zhang","year":"2018","journal-title":"arXiv 1801 01594"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09656913.pdf?arnumber=9656913","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T21:17:07Z","timestamp":1648502227000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9656913\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3137278","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}