FakeSafe: Human Level Data Protection by Disinformation Mapping Using Cycle-Consistent Adversarial Network

10 Pages Posted: 28 Sep 2020

See all articles by Dianbo Liu

Dianbo Liu

Massachusetts Institute of Technology (MIT) - MIT Computer Science and Artificial Intelligence Laboratory

He Zhu

affiliation not provided to SSRN

Date Written: August 10, 2020

Abstract

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.

Suggested Citation

Liu, Dianbo and Zhu, He, FakeSafe: Human Level Data Protection by Disinformation Mapping Using Cycle-Consistent Adversarial Network (August 10, 2020). Available at SSRN: https://ssrn.com/abstract=3671070 or http://dx.doi.org/10.2139/ssrn.3671070

Dianbo Liu (Contact Author)

Massachusetts Institute of Technology (MIT) - MIT Computer Science and Artificial Intelligence Laboratory ( email )

32 Vassar Street
Cambridge, MA 02139
United States

He Zhu

affiliation not provided to SSRN

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