Fixed-Parameter Tractability of Private Synthetic Data Generation

Abstract:We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth of the query family's incidence graph. Our algorithms attain optimal error rates across all regimes and are realized by two different approaches: the first is based on linear programming (LP) and the FPT of the separation problem for the LP dual; the second is based on a subsampled private multiplicative weights method, where we obtain FPT for sampling from Gibbs distributions. Both approaches are unified by a dynamic programming framework over a tree decomposition.

Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2606.11283 [cs.DS]

(or arXiv:2606.11283v1 [cs.DS] for this version)

https://doi.org/10.48550/arXiv.2606.11283 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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