  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.
