Generating Fake Data Using GANs for Anonymizing Healthcare Data

Abstract

EDITH is a project aiming to orchestrate an ecosystem of manipulation of reliable and safe data, applied to the field of health, proposing the creation of digital twins for personalised healthcare. This paper elaborates on a first approach about using Generative Adversarial Networks (GANs) for the generation of fake data, with the objective of anonymizing users information in the health sector. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. Meanwhile GANs are mainly exploited on images and video frames, we are proposing to process raw data in the form of an image, so it can be managed through a GAN, then decoded back to the original data domain. The performance of this prototype has been demonstrated. Moreover, a novel research pathway has been opened so further developments are expected.