Differential Privacy Data Release Scheme Using Microaggregation With Conditional Feature Selection

Abstract

Differential privacy (DP) has achieved great progress in addressing the user privacy presrvation issues related to data analysis in the Internet of Things (IoT) services and applications. However, the existing DP models tend to overlook the effect of the correlation of data features on the utility of the smart IoT data. To mitigate this gap, we propose a Microaggregation-based Differential Privacy method using Conditional Mutual Information (M-DPCMI) for pre-release data processing and feature selection. With the new method, we leverage the anonymous microaggregation approach to improve data utility while preventing potential sensitive IoT user information leakage. In addition, M-DPCMI is theoretically proved to satisfy the definition of differential privacy and experimentally validated over real datasets. It is shown that the new model achieves better data utility than the state-of-the-art DP methods.