Correlated Differential Privacy Protection for Mobile Crowdsensing

Abstract

Mobile CrowdSensing (MCS) is a new paradigm that leverages pervasive mobile devices to efficiently collect the big sensory data, enabling various large-scale applications. However, people’s concerns about the loss of individual privacy seriously hinder the prevalence of MCS applications. Differential privacy is widely focused owing to its rigorous definition and strong privacy guarantee, but the state-of-the-art studies still demonstrate its weakness on correlated data, resulting in compromising individual privacy. In this paper, we investigate the influence of sensing data correlation on differential privacy protection for MCS systems, and explore the perturbation mechanisms from two different perspectives. From a protector’s perspective, based on the Bayesian Network to model the probabilistic relationship among sensing data, we use the classical definition of differential privacy to deduce the scale parameter, and present one perturbation mechanism. From an adversary’s perspective, based on the Gaussian correlation model to describe the data correlation, we analyze the importance of the maximum correlated group to compute the Bayesian differential privacy leakage, and then provide another perturbation mechanism. Compared with the existing solutions, our mechanisms are applicable to arbitrary aggregate query function, and can avoid introducing too much noise. Moreover, we demonstrate the effectiveness of our mechanisms through extensive simulations.