Partition-based differentially private synthetic data generation

doi:10.1016/j.ins.2025.122675

Partition-based differentially private synthetic data generation

2025-09-01

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DOI：10.1016/j.ins.2025.122675

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Abstract

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Private synthetic data sharing is beneficial as it better retains the distribution and nuances of the original data compared to summary statistics such as means and frequencies. Current state-of-the-art methods follow a select-measure-generate paradigm, but measuring large-domain marginals often leads to significant errors, and managing the privacy budget poses challenges. Our partition-based approach addresses these issues, effectively reducing errors and improving the quality of synthetic data, even with a limited privacy budget. Experimental results show that our method outperforms existing approaches, yielding synthetic data with enhanced quality and utility, making it a preferred option for private data sharing.

Keywords:

Differential privacy

Synthetic data generation

Partition

Partition-based differentially private synthetic data generation

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