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

TL;DR

RON-Gauss introduces a novel method combining random orthonormal projection and Gaussian modeling to improve utility in differentially private data release, effectively supporting various machine learning tasks with strong privacy guarantees.

Contribution

The paper proposes RON-Gauss, a new approach that leverages the DFM effect and combines dimensionality reduction with Gaussian modeling to enhance utility in private data release.

Findings

Outperforms previous methods by up to ten times in utility.

Maintains small utility loss compared to non-private data.

Satisfies strong $$-differential privacy guarantees.

Abstract

A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple algorithms for a range of machine learning applications, including both unsupervised 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 for differential privacy. Finally, we illustrate the effectiveness of RON-Gauss under three common machine learning applications -- clustering, classification, and regression -- on three large real-world datasets. Our empirical results show that (a) RON-Gauss outperforms previous approaches by up to an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.