CrowdDefense: a trust vector-based threat defense model in crowdsourcing environments

On a crowdsourcing platform, in order to cheat for rewards or sabotage the crowdsourcing processes, spam workers submit numerous random and erroneous answers to the tasks published by honest requesters. This type of behaviours extremely reduces the enthusiasm of honest users, which may lead a crowdsourcing platform to a failure. To defend the threats from spam workers, reputation-based defense mechanisms and verification-based defense mechanisms have been proposed in crowdsourcing environments. However, reputation-based defense fails to indicate the trust level of a worker who boosts its reputation by transacting with his/her accomplices. In addition, verification-based defense is costly and ineffective when facing a large number of spam workers with 'good' reputations. Thus, it is a challenging problem to effectively defend the threats from spam workers. In this paper, we propose a new trust vector-based threat defense model CrowdDefense. Firstly, we build a Crowdsourcing Trust Network (CTN) consisting of requesters, workers and their transaction relations. Then, we analyze three threat patterns of spam workers. Based on the analysis, we infer the trust relation between a worker and a requester who are indirectly linked with each other. Moreover, we compute a worker's trust relations with different requesters, and present them in a new Worker Trust Vector (WTV) that indicates the worker's global trust. As spam workers always succeed in the transactions with their accomplices, they cannot obtain comprehensively good trust scores in their WTVs and thus are prevented from participating in tasks. The experiments demonstrate that CrowdsDefense significantly outperforms the state-of-the-art approaches in terms of preventing spam workers from participating in the tasks published by honest requesters.