Synthetic Data and PETs for Privacy-Compliant mHealth Within the EHDS: A Viewpoint Analysis

Abstract

The European Health Data Space (EHDS) is an initiative designed to harmonise health data sharing across Member States, with the overarching objective being to ensure compliance with the General Data Protection Regulation (GDPR). This paper examines synthetic data, generated via Variational Autoencoders (VAEs), and Privacy-Enhancing Technologies (PETs), such as Federated Learning, as solutions for privacy-preserving and interoperable mHealth systems. The utilisation of these tools is in alignment with the privacy-by-design principles outlined by the GDPR, thereby addressing the prevailing challenges associated with data sharing and regulatory compliance in the context of mHealth systems.

Keywords: European Health Data Space (EHDS); General Data Protection Regulation (GDPR); Privacy-by-Design; Synthetic Data; Variational Autoencoders (VAEs); mHealth.