Boosting Utility of Differentially Private Streaming Data Release under Temporal Correlations

Xuyang Cao, Yang Cao, Masatoshi Yoshikawa, Atsuyoshi Nakamura

Abstract: Although differentially private streaming data release has been studied extensively, how to strike a good balance between privacy and utility on correlated data is still an open problem. Many existing works focus on enhancing privacy when applying differential privacy to correlated data. They show that differential privacy may suffer extra privacy leakage under correlations, and it is inevitable to resort to a small privacy budget to prevent such privacy leakage. However, there is no attempt to solve the consequential utility problem. In this work, for the first time, we propose a post-processing framework to boost the utility of differential privacy data release under temporal correlations. Specifically, we model the problem as a maximum posterior estimation given the released differentially private data and correlation model. We finally transform this problem into a nonlinear constrained programming. Our experiments demonstrate the effectiveness of the proposed approach where the utility and accuracy of differentially private data are significantly improved by nearly ten times in terms of mean square error when a strict privacy budget is given.