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.
@article{arxiv.2310.06371,
title = {Partition-based differentially private synthetic data generation},
author = {Meifan Zhang and Dihang Deng and Lihua Yin},
journal= {arXiv preprint arXiv:2310.06371},
year = {2023}
}