SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat (reconstruction, the recovery of an individual's hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings. We systematize reconstruction (equivalently, attribute inference) attacks on de-identified and synthetic tabular data. We contribute a taxonomy that organizes attacks by the structure they exploit; a broad, controlled empirical evaluation, pitting fourteen attacks against thirteen synthetic data generation (SDG) methods across five benchmark ...
| Authors | Steven Golob, Sikha Pentyala, Martine De Cock |
|---|---|
| Primary category | cs.CR |
| Published | 6 June 2026 |
| Last updated at source | 19 September 2026 |
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