A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing
Jian Lin, Dejun Yang, Kun Wu, Jian Tang, Guoliang Xue · 2019
The rapid proliferation of sensor-embedded devices has enabled the mobile crowdsensing (MCS), a new paradigm which effectively collects sensing data from pervasive users. In order to identify the true information from the noisy data submitted by unreliable users, truth discovery algorithms have been proposed for the MCS systems to aggregate data. However, the power of truth discovery algorithms will be undermined by the Sybil attack, in which an attacker can benefit from using multiple accounts. In addition, an MCS system will be jeopardized unless it is resistant to the Sybil attack. In this paper, we proposed a Sybil-resistant truth discovery framework for MCS, which ensures high accuracy under the Sybil attack. To diminish the impact of the Sybil attack, we design three account grouping methods for the framework, which are used in pair with a truth discovery algorithm. We evaluate the proposed framework through a real-world experiment. The results show that existing truth discovery algorithms are vulnerable to the Sybil attack, and the proposed framework can effectively diminish the impact of the Sybil attack.