A New Differential Privacy Crowdsensing Scheme Based on the Multilevel Interactive Game

With the rapid growth of network of devices with embedded technology, mobile crowdsensing (MCS) has been gaining increasing popularity. The development of 5G network services is prompting further growth in crowdsensing applications. However, MCS participants risk their privacy when reporting data with their actual sensing positions. To address this issue, the concept of differential privacy (DP) can be adopted to provide a theoretical guarantee for participants’ privacy in MCS services. In this study, we design a new DP crowdsensing scheme with game theory. Based on the multilevel interactive game model, MCS server, DP controllers, and mobile devices are regarded as rational individual decision makers that aim to maximize their own payoffs. For these decision makers, the proposed game approach analyzes suitably the competitive and coordinative MCS environments. The main novelty possessed by our control scheme is to capture the dynamics of MCS system operations with the privacy consideration. Compared with other existing protocols, performance evaluation shows the advantages of our proposed scheme in terms of the sensing task success ratio, MCS participating ratio, and normalized payoff of participating devices. Finally, we provide the guidance on the future research direction of MCS services including other issues