Full Citation

Title: Partition-based differentially private synthetic data generation

Citation Type: Miscellaneous

Publication Year: 2023

ISBN: 2310.06371v1

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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.

Url: https://arxiv.org/abs/2310.06371

User Submitted?: No

Authors: Zhang, Meifan; Deng, Dihang; Yin, Lihua

Publisher: arXiv

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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