Privacy Vulnerabilities in Marginals-based Synthetic Data
<|reference_start|>Privacy Vulnerabilities in Marginals-based Synthetic Data: When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantees to this end. However, we show that the strongest class of SDG algorithms--those that preserve \textit{marginal probabilities}, or similar statistics, from the underlying data--leak information about individuals that can be recovered more efficiently than previously understood. We demonstrate this by presenting a novel membership inference attack, MAMA-MIA, and evaluate it against three seminal DP SDG algorithms: MST, PrivBayes, and Private-GSD. MAMA-MIA leverages knowledge of which SDG algorithm was used, allowing it to learn information about the hidden data more accurately, and orders-of-magnitude faster, than other leading attacks. We use MAMA-MIA to lend insight into existing SDG vulnerabilities. Our approach went on to win the first SNAKE (SaNitization Algorithm under attacK ... $\varepsilon$) competition.<|reference_end|>
@article{golob2024privacy,
    title={Privacy Vulnerabilities in Marginals-based Synthetic Data},
    author={Steven Golob, Sikha Pentyala, Anuar Maratkhan, Martine De Cock},
    journal={arXiv preprint arXiv:2410.05506},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05506},
    primaryClass={cs.CR cs.LG}
}