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