Enabling Efficient and Privacy-Preserving Task Matching For Cloud-Based Crowdsourcing

Crowdsourcing enables users (task requesters) to outsource complex tasks to an unspecified crowd of workers. To select the most appropriate task workers, the crowdsourcing platform (broker) must conduct the mutual matching between task requesters 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. To this end, we propose a privacy-preserving task matching scheme to enable the broker to conduct the mutual matching between tasks and workers according to the task requirements and worker preferences while preserving the privacy of keywords contained in the task requirements and worker preferences. We first design a privacy-preserving task matching scheme with a single keyword matching for multiple requesters and multiple workers. Specifically, new searchable encryption primitive is designed to support privacy-preserving equality matching of single keyword among multiple users. We further propose an efficient and privacy-preserving task matching scheme for conjunctive keyword matching, which not only improves the matching efficiency by batch matching but also hides the information that whether a keyword is associated with a task. The security proof shows that our proposed task matching schemes are provably secure in the random oracle model under the Bilinear Diffie-Hellman (BDH) assumption. The performance evaluation shows that the proposed scheme is efficient