Processing of synthetic data in AI development for healthcare and the definition of personal data in EU law

TL;DR

This paper examines whether synthetic health data generated for AI in healthcare should be classified as personal data under EU law, analyzing legal and technical aspects to inform regulatory clarity.

Contribution

It provides a systematic legal and empirical analysis of synthetic data's classification under GDPR, highlighting the need for clearer regulations to foster AI innovation in healthcare.

Findings

Synthetic data is likely anonymous under certain conditions

Residual identification risks depend on specific factors

Uncertainties remain about what constitutes 'reasonably likely' re-identification risk

Abstract

Artificial intelligence (AI) has the potential to transform healthcare, but it requires access to health data. Synthetic data that is generated through machine learning models trained on real data, offers a way to share data while preserving privacy. However, uncertainties in the practical application of the General Data Protection Regulation (GDPR) create an administrative burden, limiting the benefits of synthetic data. Through a systematic analysis of relevant legal sources and an empirical study, this article explores whether synthetic data should be classified as personal data under the GDPR. The study investigates the residual identification risk through generating synthetic data and simulating inference attacks, challenging common perceptions of technical identification risk. The findings suggest synthetic data is likely anonymous, depending on certain factors, but highlights uncertainties about what constitutes reasonably likely risk. To promote innovation, the study calls for clearer regulations to balance privacy protection with the advancement of AI in healthcare.