Differential Privacy-Based Incentive Scheme for App-Assisted Mobile Edge Crowdsensing

The combination of applications (Apps) and mobile edge crowdsensing technology has been viewed as a promising paradigm, where Apps are responsible for marking the location of the sensing task as point-of-interest (PoI) to assist the platform in recruiting users. However, there still exist potential incentive and privacy threats associated with App-assisted mobile edge crowdsensing (AMECS) due to the selfish nature of Apps and the vulnerability of wireless communication. To this end, we propose a differential privacy-based incentive (DPI) scheme for AMECS to support secure and efficient crowdsensing while protecting the privacy of users. Specifically, we first propose an App quality management mechanism to correlate the behavior of the App with its quality and then choose reliable Apps based on quality thresholds. Afterwards, a privacy-preserving sensing data sharing algorithm is designed to protect the privacy of users. Furthermore, given the difficulty of obtaining accurate network parameters in real life, a reinforcement learning-based incentive mechanism is devised to motivate users to actively engage in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme is effective in improving the utility of participants and protecting the privacy of users.