Privacy Preserving Efficient Worker Selection in the Cloud-Based Crowdsourcing Platform

Abstract

Crowdsourcing has become the most widely used tool to solve complex problems through the collective intelligence of distributed crowd workers, but ensuring both worker and task privacy remains a significant challenge. This research proposed a novel privacy-preserving framework, a lightweight dynamic worker selection method based on attribute-based selection that ensures the privacy of workers and tasks through pseudonymity and encryption. A two-phase encryption ensures the confidentiality and anonymity of workers and tasks against the crowd server. In addition, it incorporates efficient worker revocation to remove unreliable or spam workers without disturbing the overall schema. The detailed security analysis shows that our approach is to secure the task and worker identity with minimum complexity. An experimental study compares the proposed approach with the state-of-the-art approach, showing that it has a low computational cost and is feasible under resource-constrained environments.