Achieving Private and Fair Truth Discovery in Crowdsourcing Systems. (30th March 2022) - Wang, Zhenya; Cheng, Xiang; Su, Sen; Wang, Longhan
Abstract : Nowadays, crowdsourcing has witnessed increasing popularity as it can be adopted to solve many challenging question-answering tasks. One of the most significant problems in crowdsourcing is truth discovery, which aims to find reliable information from conflict answers provided by different workers. Despite the effectiveness for providing reliable aggregated results, existing works on truth discovery either fall short of preserving the workers' privacy or fail to consider the unfairness issue in their design. In light of this deficiency, we propose a novel private and fair truth discovery approach called PFTD, which is implemented by two non-colluding cloud servers and leverages the Paillier cryptosystem. This approach not only preserves the privacy of the answers of each worker, but also addresses the unfairness issue in crowdsourcing. Extensive experiments conducted on both real and synthetic datasets demonstrate the effectiveness of our proposed PFTD approach.