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.

中文翻译：

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生成对抗网络

生成对抗网络 (GAN) 促进了计算机视觉和自然语言处理等领域的各种应用，因为它的生成模型具有令人信服的能力，可以从现有的样本分布中生成逼真的示例。GAN 不仅在基于数据生成的任务上提供了令人印象深刻的性能，而且由于其博弈论优化策略，还促进了面向隐私和安全的研究。不幸的是，没有关于 GAN 在隐私和安全方面的全面调查，这促使本调查进行系统总结。现有作品根据隐私和安全功能进行了适当的分类，本次调查对其优缺点进行了全面分析。