Privacy-Preserving Task Recommendation Services for Crowdsourcing

Abstract

Crowdsourcing is a distributed computing paradigm that utilizes human intelligence or resources from a crowd of workers. Existing solutions of task recommendation in crowdsourcing may leak private and sensitive information about both tasks and workers. To protect privacy, information about tasks and workers should be encrypted before being outsourced to the crowdsourcing platform, which makes the task recommendation a challenging problem. In this paper, we propose a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which achieves the task-worker matching while preserving both task privacy and worker privacy. In PPTR, we first exploit the polynomial function to express multiple keywords of task requirements and worker interests. Then, we design a key derivation method based on matrix decomposition, to realize the multi-keyword matching between multiple requesters and multiple workers. Through PPTR, user accountability and user revocation are achieved effectively and efficiently. Extensive privacy analysis and performance evaluation show that PPTR is secure and efficient.