Secure and Efficient Task Matching with Multi-keyword in Multi-requester and Multi-worker Crowdsourcing

Crowdsourcing enables users (task requesters) to outsource complex tasks to an unspecified crowd of workers. To guarantee the quality of crowdsourcing service, it is necessary to select the most appropriate task workers to complete the tasks. To this end, the crowdsourcing platform (broker) must conduct the mutual matching between tasks and workers based on the task requirements and worker preferences. However, both task requirements and worker preferences may contain sensitive information (e.g., time, location of the task, etc.), which should not be revealed to the broker and other adversaries. In this paper, we propose a secure and efficient task matching scheme to enable the broker to conduct the mutual matching between tasks and workers, according to task requirements and worker preferences with multiple keywords, while preserving the privacy of keywords contained in task requirements and worker preferences. Specifically, we design a new multi-reader and multi-writer searchable encryption primitive that can support the batch matching of multiple keywords. The security proof shows that our proposed task matching scheme is provably secure in the random oracle model under the Bilinear Diffie-Hellman (BDH) assumption. The performance evaluation shows that our multi-keyword batch matching can significantly reduce the computation cost compared to existing methods.