Author

Steven Golob

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#machine learning Jun 2026

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

The choice of SDG method governs risk far more than the choice of attack; differential privacy reduces reconstruction steadily up to epsilon approximately 10, above which reconstruction risk levels off for all six DP mechanisms; de-identification methods are the most exposed, with diffusion the only synthesizer close b...

Steven Golob, Sikha Pentyala, M. De Cock · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Portable Causal Fairness Across Synthetic Data Generator Families

A new causal diffusion backbone yields the fairest release of any family the authors tested, at fidelity close to the marginals tier, and adding privacy guarantees don't make the data less fair.

Steven Golob, Sikha Pentyala, M. De Cock · 0 citations