An Insurance-Based Framework Against Security Threat in Mobile Crowdsourcing Systems

Mobile crowdsourcing is a popular computing paradigm that enables smart devices to measure and collect various sensing data. When the users participate in the sensing platform, they may face various attacks and fall in a non-secure environment. Under the malicious attack, the sensing data which should be transmitted to the platform may suffer from data loss, which reduces the users' utility and platform's utility. The data loss can also lead to the reduction of the users' participatory motivations because they are not able to earn expected money due to data loss. Therefore, the total sensing quality and the social welfare will be affected eventually. To solve this problem, in this paper, we propose a novel insurance-based framework to compensate the data loss due to security threat in mobile crowdsourcing systems. This framework can motivate the users with high-security levels to participate in the crowdsourcing system, thus improves the platform's utility. We formulate our framework as a Stackelberg game, where the platform is a leader and the users are followers. The theoretical analysis shows that the Nash Equilibrium exists in our framework and it can maximize the platform's revenue while considering the users' participatory willingness. Simulation results show that our framework achieves more participators, more platform's utility and social welfare, compared with existing mechanism.