By harvesting online workers’ knowledge, crowdsourcing has become an efficient and cost-effective way to obtain a large amount of labeled data for solving human intelligent tasks (HITs), such as entity resolution and sentiment analysis. Due to the open nature of crowdsourcing, online workers with different knowledge backgrounds may provide conflicting labels to tasks. Therefore, it is a common practice to perform a pre-determined number of assignments, either per task or for all tasks, and aggregate collected labels to infer the true label of tasks. This model could suffer from poor accuracy in case of under-budget or a waste of resource in case of over-budget. In addition, as worker labels are usually aggregated in a voting manner, crowdsourcing systems are vulnerable to strategic Sybil attack, where the attacker may manipulate several robot Sybil workers to share randomized labels for outvoting independent workers and apply various strategies to evade Sybil detection. In this thesis, we are specifically interested in providing a guaranteed aggregation accuracy with minimum worker cost and defending against strategic Sybil attack. In our first work, we assume that workers are independent and honest. By enforcing a specified accuracy threshold on aggregated labels and minimizing the worker cost under this requirement, we formulate the dual requirements for quality and cost as a Guaranteed Accuracy Problem (GAP) and present an efficient task assignment algorithm for solving the problem. In our second work, we assume that strategic Sybil attackers may coordinate Sybil workers to obtain rewards without honestly labeling tasks and apply different strategies to evade detection. By camouflaging golden tasks (i.e., tasks with known true labels) from the attacker and suppressing the impact of Sybil workers and low-quality independent workers, we extend the principled truth discovery to defend against strategic Sybil attack in crowdsorucing. For both works, we conduct comprehensive empirical evaluations on real and synthetic datasets to demonstrate the effectiveness and efficiency of our methods
