Privacy-preserving task recommendation with win-win incentives for mobile crowdsourcing

Abstract Mobile crowdsourcing enables mobile requesters to publish tasks, which can be accomplished by workers with awards. However, existing task allocation schemes face tradeoff between effectiveness and privacy preservation, and most of them lack consideration of win-win incentives for both requesters and workers participation. In this paper, we propose a privacy-preserving task recommendation scheme with win-win incentives in crowdsourcing through developing advanced attribute-based encryption with preparation/online encryption and outsourced decryption technologies. Specifically, we design bipartite matching between published tasks and participant workers, to recommend tasks for eligible workers with interests and provide valuable task accomplishment for requesters in a win-win manner. Furthermore, our scheme reduces encryption cost for requesters by splitting encryption into preparation and online phases, as well as shifts most of the decryption overhead from the worker side to the service platform. Privacy analysis demonstrates requester and worker privacy preservation under chosen-keyword attack and chosen-plaintext attack. Performance evaluation shows cost-efficient computation overhead for requesters and workers.