FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network

TL;DR

FakeSafe introduces a novel generative adversarial network approach with cycle consistency to protect private data by creating disinformation, enabling secure data transfer and storage while maintaining data utility.

Contribution

The paper presents FakeSafe, a new method employing cycle-consistent GANs for human-level data protection through disinformation mapping, addressing privacy concerns in data sharing.

Findings

Effective disinformation mapping demonstrated on benchmark datasets.

Potential for secure data transfer and storage with privacy preservation.

Applicable to real-world private data scenarios.

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.