论文标题
Fakesafe：通过使用周期矛盾的对抗网络映射的虚假信息映射的人类级别数据保护
FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network
论文作者
论文摘要
虚假信息的概念是使用假消息使人们感到困惑，以保护真实的信息。可以将该策略适应数据科学，以保护有价值的私人和敏感数据。近年来，正在从智能手机等个人设备（例如智能手机）中生成大量私人数据。能够利用这些个人数据将为设计个性化产品，执行Precision Healthcare以及过去不可能的许多其他任务带来巨大的机会。但是，由于隐私，安全和法规原因，通常很难以其原始形式传输或存储数据，同时确保它们的安全。在大多数情况下，建立安全的数据传输和存储基础架构以保护隐私是昂贵的，并且由于人为错误而始终关注数据安全性。在这项研究中，我们提出了一种名为Fakesafe的方法，旨在使用具有周期一致性的生成对抗网络提供人类水平的数据保护，并使用基准和现实世界数据集进行了实验，以说明Fakesafe的潜在应用。
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.
