Collusion Detection and Trust Management for Indoor Navigation System With Crowdsourcing

Abstract

The indoor navigation system supported by spatial crowdsourcing emerges as a promising application to provide customized location service for requesters. An important stage of crowdsourcing is to select trustworthy workers. Workers' reputation, as an essential criterion of this selection, is usually evaluated by feedback ratings from requesters. However, the reputation in the crowdsourcing-based indoor navigation system is vulnerable to the collusion attack, that is malicious workers (i.e., attackers) collude with requesters to illegally increase reputation. In this paper, we propose a collusion detection scheme to distinguish attackers and provide a secure reputation mechanism. Specifically, we first identify collusive requesters categorized into three different levels according to their feedback rating behaviors. Then, the weighted logistic regression (WLR) is developed to distinguish the collusive requesters who provide exorbitant feedback ratings. Furthermore, we employ an outlying sequence detection based on the maximum mean discrepancy (MMD), to resist the multiple location queries initiated by the same collusive requester through analyzing the distribution distance. In addition, we propose a community detection algorithm, named Fastgreedy, to identify the collusion from many requesters. Finally, the extensive simulation results demonstrate that the proposed scheme can effectively detect collusive requesters and significantly outperform other methods.