EdgeInfer: Robust Truth Inference under Data Poisoning Attack

Abstract

As crowdsourcing is becoming more widely used for annotating data from a large group of users, attackers have strong incentives to manipulate the system. Deriving the true answer of tasks in crowdsourcing systems based on user-provided data is susceptible to data poisoning attacks, whereby malicious users may intentionally or strategically report incorrect information to mislead the system into inferring the wrong truth for a set of tasks. Recent work has proposed several attacks on the crowdsourcing systems and showed that existing truth inference methods may be vulnerable to such attacks. In this paper, we propose solutions to enhance the robustness of existing truth inference methods. Our solutions base on 1) detecting and augmenting the answers for the boundary tasks in which users could not reach a strong consensus and hence are subjective to potential manipulation, and 2) enhancing inference method with a stronger prior. We empirically evaluate these defense mechanisms by designing attack scenarios that aim to decrease the accuracy of the system. Experiments show that our method is effective and significantly improves the robustness of the system under attack.