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Generative Adversarial Networks: A Survey Toward Private and Secure Applications

Authors:

Yi PanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 6

Article No.: 132, Pages 1 - 38

https://doi.org/10.1145/3459992

Published: 13 July 2021 Publication History

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Abstract

Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s 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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 6

Invited Tutorial

July 2022

799 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3475936

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 July 2021

Accepted: 01 April 2021

Received: 01 December 2020

Revised: 01 March 2020

Published in CSUR Volume 54, Issue 6

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