FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on Blockchain

【Author】 Guo, Yu; Xie, Hongcheng; Miao, Yinbin; Wang, Cong; Jia, Xiaohua

【Source】IEEE TRANSACTIONS ON SERVICES COMPUTING

【影响因子】11.019

【Abstract】Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

【Keywords】Task analysis; Crowdsourcing; Blockchain; Smart contracts; Encryption; Crowdsourcing; privacy-preserving; decentralized application; searchable encryption; blockchain

【发表时间】2022 JUL-AUG

【收录时间】2022-08-28

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-隐私计算