Sensitive Task Assignments in Crowdsourcing Markets with Colluding Workers

Crowdsourcing has raised several security concerns. One of the concerns is how to assign sensitive tasks in the crowdsourcing market, especially when there are colluding participants in crowdsourcing. In this paper, we consider adversarial colluding participants who intend to extract sensitive data by exchanging information. We design a 3-step sensitive task assignment method: (1) the collusion estimation step that quantifies the workers' pairwise collusion probability by estimating answer truth based on their responses; (2) the worker selection step that executes a heuristic sampling-based approach to select the fewest workers whose collusion probability satisfies the given security requirement; and (3) the task partitioning step that splits the sensitive information among the selected workers. We perform an extensive set of experiments on both real-world and synthetic datasets. The results demonstrate the accuracy and efficiency of our method.