Privacy-Preserving Task-Matching and Multiple-Submissions Detection in Crowdsourcing.

Crowdsourcing enables requesters to publish tasks to a platform and workers are rewarded for performing tasks of interest. It provides an efficient and low-cost way to aggregate data and solve problems that are difficult for computers but simple for humans. However, the privacy risks and challenges are still widespread. In the real world, the task content may be sensitive and only workers who meet specific requirements or possess certain skills are allowed to acquire and perform it. When these distributed workers submit their task answers, their identity or attribute privacy may also be exposed. If workers are allowed to submit anonymously, they may have the chance to repeat their answers so as to get more rewards. To address these issues, we develop a privacy-preserving task-matching and multiple-submissions detection scheme based on inner-product cryptography and proof of knowledge (PoK) protocol in crowdsourcing. In such a construction, multi-authority inner-product encryption is introduced to protect task confidentiality and achieve fine-grained task-matching based on the attributes of workers. The PoK protocol helps to restrict multiple submissions. For one task, a suitable worker could only submit once without revealing his/her identity. Moreover, different tasks for one worker are unlinkable. Furthermore, the implementation analysis shows that the scheme is effective and feasible.