Differentially Private Synthetic Data from Sliced Chebyshev Moments

Research on practical differentially private (DP) synthetic data has largely focused on methods that approximately preserve low-dimensional marginals of a target distribution, and can thus only hope to preserve higher-dimensional, global structure as a lucky by-product. At the same time, existing methods that target global accuracy guarantees pay a price exponential in the data dimension, $d$. In this work, we address this "curse of dimensionality" by presenting a new DP synthetic data algorithm, , that comes with a compelling global distributional guarantee, even in high-dimensions, and moreover, is practically state-of-the-art. Our algorithm is based on measuring noisy sliced Chebyshev polynomial moments of the target distribution and fitting a distribution to match, a twist on recent polynomial methods for 1-D synthetic data. We prove that, given a dataset of $n$ examples, returns a private distribution with Sliced Wasserstein-1 distance $\tilde{O}(d^{1/4}/\sqrt{n})$ from the target, for constant privacy parameters. So, we only need $n$ to be polynomial in the data dimension for a high-quality approximation. Along the way, we prove a moment matching result of independent interest: for a distribution on the unit ball, matching the first $O(1/\alpha )$ Chebyshev moments along $\tilde{O}(d/{\alpha }^2)$ random directions guarantees $\alpha$ Sliced Wasserstein error. Finally, we provide a practical implementation of the algorithm that borrows tools from the tensor decomposition literature and achieves state-of-the-art performance compared to prior DP synthetic data methods on an extensive benchmark.