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2020 | OriginalPaper | Chapter

Generating Fake Data Using GANs for Anonymizing Healthcare Data

Authors : Esteban Piacentino, Cecilio Angulo

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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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.

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previous chapter Anonymizing Personal Images Using Generative Adversarial Networks

next chapter A Proposal to Evolving Towards Digital Twins in Healthcare

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Title: Generating Fake Data Using GANs for Anonymizing Healthcare Data
Authors: Esteban Piacentino
Cecilio Angulo
Publisher: Springer International Publishing
Book: Bioinformatics and Biomedical Engineering
Print ISBN: 978-3-030-45384-8

Electronic ISBN: 978-3-030-45385-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-45385-5_36

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