A Comprehensive Location-Privacy-Awareness Task Selection Mechanism in Mobile Crowd-Sensing

Abstract

With the boom in the production of smartphones and easy access to networks, mobile crowd-sensing (MCS) is becoming a promising and rapidly growing sensing paradigm for the Internet of Things (IoT). In MCS, workers can get rewarded by participating in data collection while traveling to their destination from some starting point. However, the privacy leakage becomes a serious and unavoidable problem that hinders users' engagement. Taking task at a specific location or even going through some seemingly innocent locations may both disclose user's sensitive location information. Simply concealing or obfuscating the sensitive locations could preserve the privacy to a certain extent, at the cost of reward reduction, which will significantly frustrate the workers. To address these issues, this paper provides a novel framework for task selection in MCS jointly covering comprehensive location privacy preservation, efficient resource consumption, and high task profits. In our framework, workers can select their paths freely, and customize their privacy requirements for protection against both direct location disclosure and destination inference attack. Two corresponding algorithms are proposed for path selection under various stages of privacy preservation. The theoretical analysis demonstrates the performance of two algorithms on both privacy and utility aspects. We also evaluate our proposed mechanisms through extensive experiments on a real-world data set. The results validate that the proposed mechanisms can achieve higher task earnings and better privacy preservation.