Differentially Private Synthetic Data via APIs 4: Tabular Data

2026-06-06

AI Summary

This work addresses the challenge of preserving high-order variable correlations in synthetic tabular data under differential privacy. The authors propose Tab-PE, the first algorithm to extend the Private Evolution framework to tabular data generation. Tab-PE introduces low-overhead heuristic evolutionary operators that efficiently optimize candidate datasets within differential privacy constraints, combined with a private scoring and selection mechanism to simultaneously capture complex correlations and ensure scalability. Experimental results demonstrate that Tab-PE significantly outperforms existing methods on both real-world and synthetic datasets, achieving up to a 10% improvement in classification accuracy over the AIM baseline while running 28 times faster.

Abstract

This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive study, state-of-the-art methods often focus on minimizing low-order marginal query errors and overlook the challenges posed by high-order correlations. To address this gap, we extend the Private Evolution (PE) framework, originally developed for DP-compliant image and text synthesis, to tabular data. We introduce Tab-PE -- an algorithm for synthetic tabular data generation under DP constraints. Tab-PE iteratively improves a candidate dataset via an evolutionary process that leverages tabular-specialized operators to produce variations, privately scores them, and selects the highest-quality samples to retain and propagate. In contrast to the original PE, which relies on large foundation models, Tab-PE employs heuristic operators with significantly lower computational costs, making PE more practical and scalable for tabular data. Through extensive experiments on real-world and simulation datasets, we demonstrate that Tab-PE substantially outperforms prior baselines on datasets exhibiting high-order correlations. Compared to the best baseline -- AIM, Tab-PE improves classification accuracy by up to 10% while running 28 times faster.

Problem

• differential privacy
• synthetic tabular data
• high-order correlations
• data sharing
• privacy-preserving

Innovation

• Differential Privacy
• Synthetic Tabular Data
• Private Evolution
• High-order Correlations
• Heuristic Operators