Journal №1 (2024)
Legal and Regulatory Perspectives on Synthetic Data as an Anonymization Strategy
∘ Alexander Boudewijn ∘ Andrea F. Ferraris ∘

In an increasingly digital world, the protection of personal data is paramount for individuals, organizations, and regulators. As data collection technologies evolve, so must methods for ensuring data privacy. This paper explores synthetic data as a promising privacy-enhancing technology (PET) for anonymization, focusing on legal, theoretical, and practical perspectives. Synthetic data, generated algorithmically, do not pertain to real individuals, making them valuable for data science and AI development while preserving privacy. We examine the regulatory context, particularly under the GDPR, and identify privacy risks and attacks that anonymization must defend against. We argue that synthetic data, when properly generated, can meet anonymization standards and provide deployment recommendations to mitigate privacy risks. Our findings contribute to a standardized framework for synthetic data privacy assurance, aligning with current and future data protection regulations.

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