On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics

Researchers demonstrate significant privacy vulnerabilities in tabular diffusion models (TDMs), which are increasingly used to generate synthetic data as privacy-preserving alternatives. Through membership inference attacks in both black-box and white-box settings, the study reveals that attackers can successfully breach these systems without perfect knowledge of training data or massive computational resources, while also exposing flaws in commonly-used privacy metrics.

Tabular diffusion models have emerged as a promising solution for organizations seeking to share sensitive data while minimizing privacy risks. These models generate synthetic versions of real datasets that maintain statistical properties while theoretically protecting individual records. However, this research fundamentally challenges that assumption by demonstrating that membership inference attacks—which determine whether specific data points were used in training—can succeed against TDMs with limited attacker resources or knowledge.

The findings are particularly concerning because they operate under realistic threat scenarios. Previous privacy research often assumes adversaries have complete information about model training and access to identical data distributions. This work proves such assumptions are unnecessary; attackers achieving success with partial knowledge fundamentally changes the threat landscape. The distinction between black-box and white-box attack success rates also reveals that even limited model access creates exploitable vulnerabilities.

For organizations relying on TDMs for regulatory compliance or data monetization, these results signal a critical gap between perceived and actual privacy protection. Industries handling healthcare, financial, or personal information face regulatory pressures to minimize data exposure, making TDMs attractive. However, if synthetic data generation doesn't provide the promised privacy guarantees, organizations risk compliance violations and reputational damage from breaches.

The research additionally invalidates heuristic privacy metrics like distance-to-closest record, which many practitioners use to assess privacy adequacy. This suggests current evaluation frameworks are inadequate, requiring more rigorous privacy auditing before TDM-generated data enters production environments. Organizations must reassess their synthetic data strategies and demand stronger privacy guarantees backed by formal cryptographic proofs rather than heuristic measures.

• →Membership inference attacks successfully compromise tabular diffusion models even with incomplete attacker knowledge or computational constraints.
• →Common privacy metrics like distance-to-closest record provide false confidence and inadequately measure actual privacy leakage.
• →Both black-box and white-box attack scenarios pose serious threats, with white-box attacks demonstrating particularly severe vulnerabilities.
• →Organizations using TDMs for sensitive data sharing may have insufficient privacy protection despite regulatory compliance assumptions.
• →Stronger formal privacy guarantees are needed beyond current heuristic measures for safe synthetic data deployment.