we present a highly effective algorithmic approach, pmm, for generating differentially private synthetic data in a bounded metric space with near-optimal utility guarantees under the 1-wasserstein distance. in particular, for a dataset in the hypercube [0,1]^d, our algorithm generates synthetic dataset such that the expected 1-wasserstein distance between the empirical measure of true and synthetic dataset is o(n^{-1/d}) for d>1. our accuracy guarantee is optimal up to a constant factor for d>1, and up to a logarithmic factor for d=1. also, pmm is time-efficient with a fast running time of o(\epsilon d n). derived from the pmm algorithm, more variations of synthetic data publishing problems can be studied under different settings.