Partition-based differentially private synthetic data generation
Meifan Zhang Dihang Deng Lihua Yin
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.