COMP: Online Control Mechanism for Profit Maximization in Privacy- Preserving Crowdsensing

Abstract

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide their data if their sensing data expose their personal private information or even lead to malicious attacks. Additionally, it is still challenging for the platform to consider the randomness of sensing task arrival, the dynamic participation of participants and the complexity of task allocation. To this end, an online control mechanism is presented to maximize the profit of platform while guaranteeing system stability and providing personalized location privacy protection. By exploiting Lyapunov optimization theory, we transform the optimization problem into a queue stability problem, decomposing it into three subproblems further. Through rigorous theoretical analysis, we prove that our time-averaged profit is approximately optimal. We also carry out extensive simulations to verify the superiority of our proposed mechanism.