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

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250531

Authors: Francesco Capparelli, Maria Rosaria de Ligio, Giulia Finocchiaro

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4410447256

FWCI:

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