A Privacy-Preserving Incentive Scheme for Data Sensing in App-Assisted Mobile Edge Crowdsensing

Abstract

Application (App)-assisted mobile edge crowd- sensing is a promising paradigm, in which Apps are in charge of tagging the location of the sensing tasks as point-of-interest (PoI) to assist the platform in recruiting users to participate in the sensing tasks. However, there exist potential security, incentive, and privacy threats for App-assisted mobile edge crowdsensing (AMECS) due to the presence of malicious Apps, the low-quality shared sensing data, and the vulnerability of wireless communication. Therefore, we propose a differential privacy-based incentive (DPI) scheme for AMECS to provide secure and efficient crowdsensing services while protecting users’ privacy. Specifically, we first propose an App quality management mechanism to correlate the behavior of each App with its quality and then select reliable Apps based on quality thresholds to assist the platform in recruiting users. With the designed mechanism, we further present an auction game-based incentive mechanism to encourage Apps to mark the location of the sensing tasks as PoI. To protect the privacy of users, a privacy-preserving sensing data sharing algorithm is devised based on differential privacy. Further, given the difficulty of obtaining accurate network parameters in practice, a reinforcement learning-based incentive mechanism is designed to encourage users to participate in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme can effectively improve the utilities of users, ensure the security of the crowdsensing process, and protect the privacy of users.