Age of Information Optimization for Privacy-Preserving Mobile Crowdsensing
Yaoqi Yang, Bangning Zhang, Daoxing Guo, Renhui Xu, Chunhua Su, Weizheng Wang
Abstract
Mobile crowdsensing (MCS)-enabled data collection can be implemented in a cost-effective, scalable, and flexible manner. However, joint sensing data freshness and security assurance have not been fully investigated in the current research. To address these two concerns, the potential game and homomorphic encryption-based joint Age of Information (AoI) optimization and privacy-preservation scheme for MCS is put forward in this paper. At first, the AoI minimization and privacy preservation-oriented MCS system framework is established. Then, the AoI-based spectrum access strategies are derived by a potential game in detail, where the stochastic learning algorithm is used to reach the Nash Equilibrium (NE) solution. Next, based on the somewhat homomorphic encryption method, the encrypted sensing data can be submitted to the service provider (SP) for further processing, where the data content can only be known to mobile workers (MWs) and service requester (SR) with permission. Finally, the numerical results show that our proposed MCS system can simultaneously guarantee data freshness and system security at an acceptable cost.