SybSub: Privacy-preserving expressive task subscription with sybil detection in crowdsourcing

• Main Authors: SHU, Jiangang, LIU, Ximeng, YANG, Kan, ZHANG, Yinghui, JIA, Xiaohua, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/4677
• Institution: Singapore Management University

The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. Task matching or task subscription is one of indispensable services in crowdsourcing, but few mechanisms can achieve the expressive task subscription while protecting the privacy. In this paper, we focus on the privacy leaks and attacks during task subscription in crowdsourcing, and propose a privacy-preserving task subscription scheme with sybil detection, called SybSub. The SybSub scheme achieves the expressiveness of task subscription in the multisubscriber and multipublisher crowdsourcing while protecting the privacy of both subscribers and publishers against the semi-honest crowdsourcing service provider, and meanwhile supports the sybil attack detection against greedy subscribers. We implement the SybSub scheme and evaluate it thoroughly. Performance results validate that the SybSub scheme is efficient and feasible.