Trust management in crowdsourcing environments

As a cost-effective model for solving problems, crowdsourcing has been widely applied in various human intelligence tasks, such as data labeling, data translation, and prediction. However, without adequate trust management, a large number of untrustworthy workers submit low-quality or even junk answers in the tasks to benefit themselves or sabotage their competitors' crowdsourcing processes. The disturbance or attacks not only significantly increase the cost of solving a task, but also drastically reduce the effectiveness of crowdsourcing processes. Therefore, selecting trustworthy workers to participate in tasks has become a top-priority demand in crowdsourcing environments. To achieve an effective trustworthy worker selection, three challenging sub-problems including context-aware trust evaluation, spam worker defense, and trustworthy worker recommendation have to be tackled. As such, in this thesis, we systematically propose our solutions for the three sub-challenges. The main contributions are summarized as follows. In a crowdsourcing platform, a worker's trustworthiness varies in different contexts, complicating the trust evaluation of a crowdsourcing worker. Thus, we propose a new context-aware trust model that evaluates a worker's trust in two primary crowdsourcing contexts, i.e., the context of task type and the context of reward amount, respectively. In particular, we first propose a task type taxonomy and a task reward amount taxonomy. Based on them, we devise two novel context-aware trust metrics: Task Type-aware Trust (TaTrust) and Reward Amount-aware Trust (RaTrust). Finally, we devise a multi-objective combinatorial optimization algorithm to effectively select trustworthy workers. To defend against the threats from the spam workers who masquerade themselves as "trustworthy" workers with "good" reputations by colluding with their accomplices, we propose a new spam worker defense model based on our proposed Worker Trust Vector (WTV). A WTV consisting of the trust opinions from different requesters ...