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A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Authors present Anonymeter, a statistical framework to jointly quantify different types of privacy risks in synthetic tabular datasets. We equip this framework with attack-based evaluations for the singling out, linkability, and inference risks, which are the three key indicators of factual anonymization according to data protection regulations, such as the European General Data Protection Regulation (GDPR).

They demonstrate the effectiveness of used methods by conducting an extensive set of experiments that measure the privacy risks of data with deliberately inserted privacy leakages, and of synthetic data generated with and without differential privacy.

Researcers observe that synthetic data exhibits the lowest vulnerability against linkability, indicating one-to-one relationships between real and synthetic data records are not preserved. Finally, with a quantitative comparison they demonstrate that Anonymeter outperforms existing synthetic data privacy evaluation frameworks both in terms of detecting privacy leaks, as well as computation speed.

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