Privacy-preserving mobile crowdsensing for located-based applications

Mobile crowdsensing is a new paradigm which explores the mobility and intelligence of mobile users to collect high-quality data from social events and phenomena for conducting complex sensing tasks. Nevertheless, privacy preservation and task allocation become main obstacles that need additional attention. To achieve accurate task allocation, it is inevitable to share some sensitive information of mobile users and customers, such as identities, location and points of interest. In this paper, we propose a privacy-preserving mobile crowdsensing framework (PPMC) for location-based applications to balance the tradeoff between privacy preservation and task allocation. In PPMC, we develop a matrix-based location matching mechanism for the service provider to achieve location-based task allocation without disclosing the location of mobile users and the sensing area of tasks. We also extend BBS+ signature and proxy reencryption to preserve identity privacy and data privacy for both customers and mobile users under the condition that they are honest to release and perform tasks, respectively. Finally, we discuss security properties and demonstrate the efficiency of PPMC in terms of computational and communication overhead.