Privacy-Preserving Outsourced Task Scheduling in Mobile Crowdsourcing

With the proliferation of smart devices with various onboard sensors, mobile crowdsourcing has attracted consider-able interest, in which task scheduling is an essential service for allocating tasks to workers. As the number of workers increases, the service provider prefers to outsource the service to a powerful cloud, and the messages from the workers and the task owners should be protected. Currently, the existing works on privacy-preserving task allocation cannot simultaneously achieve strong privacy protection and dynamic update. Aiming at this challenge, we propose a privacy-preserving outsourced task scheduling scheme, in which the cloud servers can obliviously conduct task scheduling and update the workers' information based on the scheduling results. To this end, we design three secure protocols under a two-server model, which can protect the protocols' input and output against the cloud servers. The security analysis demonstrates that our proposed scheme can achieve strong privacy protection, and the experimental results indicate the scheme's efficiency in computing and communication.