Joint Data Freshness Optimization and Privacy Preservation in Mobile Crowdsensing

Abstract

To efficiently and reliably obtain the target data, mobile crowdsensing (MCS) is widely used to provide the sensing data collection service. Currently, despite concerns of sensing network scale and mobility can be addressed to some degree in the MCS manner, the freshness and privacy goals of the sensing data are still not considered simultaneously. We combine the Age of Information (AoI) with security-enhanced technique to establish a novel MCS system, which can guarantee data freshness and security, respectively. Hence, the problem is formatted as a joint AoI optimization and privacy-preservation process. To solve this problem, we utilize game theory to achieve AoI-oriented spectrum access, and homomorphic encryption to encrypt communication data. Finally, security analysis and numerical results show that our proposed approach can effectively ensure the security level and improve the AoI performance at the same time in MCS.