Systematization of Reconstruction Attacks on Synthetic Tabular Data
Researchers have conducted the first comprehensive study of reconstruction attacks—attempts to recover hidden attributes of individuals from synthetic data releases—evaluating fourteen attacks across nine synthetic data generation methods. The work, which won first place in a NIST competition, reveals that the choice of synthetic data generation method is the primary factor governing privacy risk, while differential privacy protection plateaus above small privacy budgets. This matters because synthetic data is increasingly used as a privacy-preserving alternative to releasing real sensitive records, but understanding its vulnerabilities is critical for protecting individual privacy.
A team of researchers has published a systematization of knowledge (SoK) on reconstruction attacks targeting synthetic tabular data, introducing a comprehensive taxonomy of attacks organized by the structural vulnerabilities they exploit. The study represents the most extensive empirical evaluation to date, testing fourteen different attacks against nine synthetic data generation methods across five benchmark datasets, and introduces new attacks including CoBP-RA, identified as the strongest measured attack. The researchers developed a novel methodology to distinguish between attacks that recover population-level distributional patterns versus those that memorize specific training records, placing reconstruction and membership inference attacks on a comparable scale. Key findings indicate that the synthetic data generation method chosen has far greater impact on privacy risk than the specific attack used, differential privacy provides meaningful protection only at small privacy budgets (ε ≲ 1) before protection plateaus, and de-identification methods are particularly vulnerable. The work was externally validated through a first-place finish in the 2025 NIST Collaborative Research Cycle red team competition, lending credibility to the findings.
What different sources said
- arXiv cs.LGCenter
SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
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