The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable private and sensitive data. Huge amount of private data are being generated from personal devices such as smart phone and wearable in recent years. Being able to utilize these personal data will bring big opportunities to design personalized products, conduct precision healthcare and many other tasks that were impossible in the past. However, due to privacy, safety and regulation reasons, it is often difficult to transfer or store data in its original form while keeping them safe. Building a secure data transfer and storage infrastructure to preserving privacy is costly in most cases and there is always a concern of data security due to human errors. In this study, we propose a method, named FakeSafe, to provide human level data protection using generative adversarial network with cycle consistency and conducted experiments using both benchmark and real world data sets to illustrate potential applications of FakeSafe.

中文翻译：

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FakeSafe：使用周期一致的对抗网络通过信息映射进行人级数据保护

虚假信息的概念是使用虚假消息来混淆人们，以保护真实信息。可以将此策略应用于数据科学，以保护宝贵的私有和敏感数据。近年来，从诸如智能电话之类的可穿戴设备中生成了大量的私人数据。能够利用这些个人数据将为设计个性化产品，进行精确的医疗保健以及过去无法完成的许多其他任务带来巨大机遇。但是，由于隐私，安全和法规方面的原因，在保持数据安全的同时，通常很难以其原始格式传输或存储数据。在大多数情况下，建立安全的数据传输和存储基础结构以保护隐私非常昂贵，并且由于人为错误，始终存在数据安全性问题。在这个研究中，