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Surv."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model\u2019s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.<\/jats:p>","DOI":"10.1145\/3459992","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T16:48:08Z","timestamp":1626194888000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":344,"title":["Generative Adversarial Networks"],"prefix":"10.1145","volume":"54","author":[{"given":"Zhipeng","family":"Cai","sequence":"first","affiliation":[{"name":"Georgia State University, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuobin","family":"Xiong","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghui","family":"Xu","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, CAS, China and Georgia State University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2855136"},{"key":"e_1_2_1_3_1","volume-title":"GAMIN: An adversarial approach to black-box model inversion. arXiv:1909.11835.","author":"A\u00efvodji Ulrich","year":"2019"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2018.2888775"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the AMIA Annual Symposium. 21--25","author":"Aliferis Constantin F.","year":"2003"},{"key":"e_1_2_1_6_1","volume-title":"Emotionless: Privacy-preserving speech analysis for voice assistants. arXiv:1908.03632.","author":"Aloufi Ranya","year":"2019"},{"key":"e_1_2_1_7_1","unstructured":"Mart\u00edn Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. 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