Partition-based differentially private synthetic data generation

• URL: http://arxiv.org/abs/2310.06371v1
• Date: Tue, 10 Oct 2023 07:23:37 GMT
• Title: Partition-based differentially private synthetic data generation
• Authors: Meifan Zhang, Dihang Deng, Lihua Yin
• Abstract summary: We present a partition-based approach that reduces errors and improves the quality of synthetic data, even with a limited privacy budget. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing.
• Score: 0.5095097384893414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-measure-generate paradigm, but measuring large domain marginals still results in much error and allocating privacy budget iteratively is still difficult. To address these issues, our method employs a partition-based approach that effectively reduces errors and improves the quality of synthetic data, even with a limited privacy budget. Results from our experiments demonstrate the superiority of our method over existing approaches. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing.

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