Fair Incentive Mechanism With Imperfect Quality in Privacy-Preserving Crowdsensing

Mobile crowdsensing (MCS) enables a platform to recruit users to collectively perform sensing tasks from requesters. In order to maximize the completion qualities of tasks, an incentive mechanism should be well designed for the platform to incentivize high-quality users' participation. The existing works largely adopt the Stackelberg game to model the strategic interactions in the incentive mechanism. However, there are practical issues that are less investigated in the context of the Stackelberg-based incentive mechanism. First, the platform has no knowledge about users' sensing qualities beforehand due to their private information. Second, the platform needs users' continuous participation in the long run, which results in fairness requirements. Third, it is also crucial to protect users' privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. In this article, we jointly address these issues and propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multiarmed bandits (CVMABs) to elicit unknown users' sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves sublinear regret performance to learn unknown quality with fairness and privacy guarantee.