Researchers Establish Fixed-Parameter Tractability for Private Synthetic Data Generation
Computer scientists have proven that generating synthetic data under differential privacy constraints is computationally tractable when parameterized by the treewidth of a query family's incidence graph. The work presents two algorithmic approaches—one based on linear programming and another using subsampled private multiplicative weights—both achieving optimal error rates. This theoretical result advances the understanding of how to generate privacy-preserving synthetic datasets efficiently.
A new arXiv paper establishes fixed-parameter tractability (FPT) for the problem of generating synthetic data under differential privacy constraints, with the parameter being the treewidth of the query family's incidence graph. The researchers present two distinct algorithmic approaches: the first leverages linear programming and FPT results for the separation problem in the LP dual, while the second employs a subsampled private multiplicative weights method that achieves FPT for sampling from Gibbs distributions. Both approaches are unified through a dynamic programming framework operating over tree decompositions. The algorithms attain optimal error rates across all regimes, suggesting practical applicability. This theoretical contribution addresses a fundamental problem in privacy-preserving data synthesis, where balancing utility and privacy guarantees remains challenging.
What's missing
The paper does not discuss empirical validation or experimental results on real-world datasets, focusing instead on theoretical guarantees. Additionally, practical runtime comparisons between the two proposed approaches and existing methods are not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Fixed-Parameter Tractability of Private Synthetic Data Generation
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