Anonymous Privacy-Preserving Task Matching in Crowdsourcing

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

43 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)3068-3078
Journal / PublicationIEEE Internet of Things Journal
Volume5
Issue number4
Online published27 Apr 2018
Publication statusPublished - Aug 2018

Abstract

With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multi-requester/multi-worker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, but also achieves query traceability against dishonest or revoked workers. Detailed privacy analysis and thorough performance evaluation show that the proposed scheme is secure and feasible.

Research Area(s)

  • anonymity, Crowdsourcing, Encryption, Internet of Things, privacy, Public key, revocation, Task analysis, task matching, traceability

Citation Format(s)

Anonymous Privacy-Preserving Task Matching in Crowdsourcing. / Shu, Jiangang; Liu, Ximeng; Jia, Xiaohua et al.

In: IEEE Internet of Things Journal, Vol. 5, No. 4, 08.2018, p. 3068-3078.

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