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A key challenge facing the design of differential privacy in thenon-interactive setting is to maintain the utility of the released data. Toovercome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect,which states that most projections of high-dimensional data are nearlyGaussian. Hence, we propose the RON-Gauss model that leverages the novelcombination of dimensionality reduction via random orthonormal (RON) projectionand the Gaussian generative model for synthesizing differentially-private data.We analyze how RON-Gauss benefits from the DFM effect, and present multiplealgorithms for a range of machine learning applications, including bothunsupervised and supervised learning. Furthermore, we rigorously prove that (a)our algorithms satisfy the strong $\epsilon$-differential privacy guarantee,and (b) RON projection can lower the level of perturbation required fordifferential privacy. Finally, we illustrate the effectiveness of RON-Gaussunder three common machine learning applications -- clustering, classification,and regression -- on three large real-world datasets. Our empirical resultsshow that (a) RON-Gauss outperforms previous approaches by up to an order ofmagnitude, and (b) loss in utility compared to the non-private real data issmall. Thus, RON-Gauss can serve as a key enabler for real-world deployment ofprivacy-preserving data release.

RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release