BFCRI: A Blockchain-Based Framework for Crowdsourcing With Reputation and Incentive
Shaojing Fu, XueLun Huang, Lin Liu, Yuchuan Luo
Abstract
With the rapid development of cloud computing and the sharing economy, crowdsourcing aroused widespread interest and adoption in providing intelligent and efficient services for humans. The majority of existing works focus on effective crowdsourcing task assignment and privacy protection, mostly relying on central servers and assuming that participants are <inline-formula><tex-math notation="LaTeX">$honest$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$and$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$curious$</tex-math></inline-formula> and proactive. However, in reality, workers may be unwilling to participate, and there may be malicious behavior among participants, thus harming the enthusiasm and interests of other participants. The central server has weaknesses such as single point of failure. To address above problems, we propose a blockchain-based framework for crowdsourcing with reputation and incentive. We first design a worker selection scheme to select credible and capable workers. We leverage reputation as a metric of workers’ credibility, which is calculated through the improved subjective logic model. Then we utilize contract theory to design incentive mechanisms to attract more workers, especially high-quality workers to participate. Experimental results show that our proposed method can detect and prevent malicious participants and resist malicious collusion when the proportion of malicious participants is no more than 1/3. And encourage more workers to actively, honestly and continuously participate in crowdsourcing.