An Efficient Privacy-Preserving Incentive Scheme without TTP in Participatory Sensing Network

Jingwei Liu, Xiaolu Li, Rong Sun, Xiaojiang Du, Paul Ratazzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Along with the development of wireless communication technology, a mass of mobile devices are gaining stronger sensing capability, which brings a novel paradigm to light: participatory sensing networks (PSNs). PSNs can greatly reduce the cost of wireless sensor networks, and hence are becoming an efficient way to obtain abundant sensing data from surrounding environment. Therefore, PSNs would lead to significant improvement in various fields, including cognitive communication. However, the large-scale deployment of participatory sensing applications is hindered by the lack of incentive mechanism, security and privacy concerns. It is still an ongoing issue to address all three aspects simultaneously in PSNs. In this paper, we construct an efficient privacy-preserving incentive scheme without trusted third party (TTP) for PSNs to motivate user-participation. This scheme allows each participant to earn credits by contributing data privately. Using blind and partially blind signatures, the proposed scheme is proved to be secure for privacy and incentive. Additionally, the performance evaluation in terms of computation and storage indicates that the proposed scheme has higher efficiency.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
DOIs
StatePublished - 27 Jul 2018
Event2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
Duration: 20 May 201824 May 2018

Publication series

NameIEEE International Conference on Communications
Volume2018-May
ISSN (Print)1550-3607

Conference

Conference2018 IEEE International Conference on Communications, ICC 2018
Country/TerritoryUnited States
CityKansas City
Period20/05/1824/05/18

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