Data-Driven Approach for Evaluating Risk of Disclosure and Utility in Differentially Private Data Release

Differential privacy (DP) is a popular technique for protecting individual privacy and at the same for releasing data for public use. However, very few research efforts are devoted to the balance between the corresponding risk of data disclosure (RoD) and data utility. In this paper, we propose data-driven approaches for differentially private data release to evaluate RoD, and offer algorithms to evaluate whether the differentially private synthetic dataset has sufficient privacy. In addition to the privacy, the utility of the synthetic dataset is an important metric for differentially private data release. Thus, we also propose the data-driven algorithm via curve fitting to measure and predict the error of the statistical result incurred by random noise added to the original dataset. Finally, we present an algorithm for choosing appropriate privacy budget epsilon with the balance between the privacy and utility.