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Mobile Information Systems
Volume 2016 (2016), Article ID 6506341, 10 pages
http://dx.doi.org/10.1155/2016/6506341
Research Article

Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System

Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan

Received 7 August 2015; Accepted 16 February 2016

Academic Editor: Jong-Hyouk Lee

Copyright © 2016 Shih-Hao Chang and Zhi-Rong Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Mobile crowd sensing (MCS) arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely, Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attack, which illegally forges multiple identities in peer-to-peer networks, namely, Sybil identities. These Sybil identities will falsify multiple identities that negatively influence the effectiveness of sensing data in this MCS network or degrading entire network performance. To cope with this problem, a cloud based trust management scheme (CbTMS) was proposed to detect Sybil attacks in the MCS network. The CbTMS was proffered for performing active and passive checking scheme, in addition to the mobile PCS trustworthiness management, and includes a decision tree algorithm, to verify the covered nodes in the MCS network. Simulation studies show that our CbTMS can efficiently detect the malicious Sybil nodes in the network and cause 6.87 Wh power reduction compared with other malicious Sybil node attack mode.