SybSub : Privacy-Preserving Expressive Task Subscription with Sybil Detection in Crowdsourcing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

5 Scopus Citations
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Author(s)

  • Ximeng Liu
  • Kan Yang
  • Yinghui Zhang
  • Robert H. Deng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3003-3013
Journal / PublicationIEEE Internet of Things Journal
Volume6
Issue number2
Online published24 Oct 2018
Publication statusPublished - Apr 2019

Abstract

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 multi-subscriber and multi-publisher 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.

Research Area(s)

  • Crowdsourcing, crowdsourcing, Encryption, expressiveness, Internet of Things, Numerical models, Privacy, privacy-preserving., sybil detection, Task analysis, Task subscription