Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the related legal theory. We then apply the framework to the main privacy quantification methods with no-box threat models on publicly available datasets.
@article{arxiv.2512.16284,
title = {Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework},
author = {Milton Nicolás Plasencia Palacios and Alexander Boudewijn and Sebastiano Saccani and Andrea Filippo Ferraris and Diana Sofronieva and Giuseppe D'Acquisto and Filiberto Brozzetti and Daniele Panfilo and Luca Bortolussi},
journal= {arXiv preprint arXiv:2512.16284},
year = {2026}
}