Privacy-preserving mobile crowd sensing for big data applications

Mobile crowd sensing presents a new sensing paradigm which allows an individual participant with mobile device perform sensing tasks and activities of professional organizations. Privacy is one major concern in the mobile crowd sensing application. In this paper, we first present a fog-assisted mobile crowd sensing architecture, then we propose two privacy-preserving crowd sensing schemes for two categories of crowd sensing applications. The first scheme is based on an additive homomorphic encryption algorithm and allows the service subscriber collect the statistical data without revealing the individual data from each participant. The second scheme is based on a bitwise-XOR homomorphic encryption algorithm and allows the service subscriber collect the accurate data without know which data is from which participant. The proposed schemes can achieve κ-anonymity privacy level for each participant. We also give the performance analysis of the proposed schemes.