Differentially Private Synthetic Data via APIs 4: Tabular Data

• URL: http://arxiv.org/abs/2606.08259v1
• Date: Sat, 06 Jun 2026 17:08:41 GMT
• Title: Differentially Private Synthetic Data via APIs 4: Tabular Data
• Abstract summary: State-of-the-art methods often focus on minimizing low-order marginal query errors and overlook the challenges posed by high-order correlations.<n>To address this gap, we extend the Private Evolution framework, originally developed for DP-compliant image and text synthesis.
• Score: 14.625539540842672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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