RCTD: Reputation-Constrained Truth Discovery in Sybil Attack Crowdsourcing Environment

Sybil attacks are a prevalent concern within the realm of crowdsourcing, underscoring the significance of quality control in this domain. Truth discovery has been extensively studied to deduce the most trustworthy information from conflicting data based on the principle that reliable workers yield reliable answers. However, existing truth discovery approaches overlook the metric of workers' reputations, e.g., workers' historical approval rates on crowdsourcing platforms, despite being inflated and noisy, they offer a rough indication of workers' ability. In this paper, we first refine the approval rate using Wilson Lower Bound to enhance its confidence, and then mitigate its noise and inflation through a method based on ranking similarity. Specifically, we propose a method called RCTD (Reputation-Constrained Truth Discovery), which introduces a similarity metric between the rankings of workers' weights and the refined approval rates. This metric serves as a penalizing factor in the objective function of the truth discovery, restricting workers' weights to avoid excessively deviating from their historical reputation during the weight estimation process. We solve the objective function by introducing the block coordinate descent coupled with heuristics approach method. Experimental results on real-world datasets demonstrate that our approach achieves more accurate inference of true results in the Sybil attack environment compared to the state-of-the-art methods.