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Sybil Defense in Crowdsourcing Platforms

Crowdsourcing platforms have been widely deployed to solve many computer-hard problems, e.g., image recognition and entity resolution. Quality control is an important issue in crowdsourcing, which has been extensively addressed by existing quality-control algorithms, e.g., voting-based algorithms and probabilistic graphical models. However, these algorithms cannot ensure quality under sybil attacks, which leverages a large number of sybil accounts to generate results for dominating answers of normal workers. To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack. We develop a similarity function to quantify worker similarity. Based on worker similarity, we cluster workers into different groups such that we can utilize a small number of golden questions to accurately identify the sybil groups. We also devise online algorithms to instantly detect sybil workers to throttle the attacks. Our method also has ability to detect multi-attackers in one task. To the best of our knowledge, this is the first framework for sybil defense in crowdsourcing. Experimental results on real-world datasets demonstrate that our method can effectively identify and throttle sybil workers.