Blockchain-Based Lightweight and Privacy-Preserving Quality Assurance Framework in Crowdsensing Systems

Xiaodong Shen, Chang Xu*, Liehuang Zhu, Rongxing Lu, Yunguo Guan, Xiaoming Zhang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

5 引用 (Scopus)

摘要

The novel sensing paradigm known as crowdsensing leverages ubiquitous smart devices to collect data in Internet of Things (IoT) applications. Traditional crowdsensing schemes assume a central framework to execute truth discovery algorithm to assure data quality, which may introduce reliability and privacy issues. Blockchain is a promising technology that provides a decentralized, transparent, and immutable platform. However, designing a blockchain-based quality assurance scheme in crowdsensing is not a trivial problem. First, truth discovery is a time-consuming iterative algorithm, which is not practical to execute on blockchain. Second, privacy-preserving schemes always require that the participants join in multiround communications, which is not acceptable in open blockchain because of users' highly unpredictable behaviors. Finally, on-chain data are publicly accessible, and achieving a good balance between data utility and privacy is an important issue. In this article, we propose a lightweight quality assurance framework atop blockchain to build a reliable, privacy preserving, and fair crowdsensing system. Specifically, we carefully design two kinds of smart contracts to cooperatively maintain a long-term reliable platform to execute crowdsensing tasks. In the contracts, we devise a reputation-based aggregator selection algorithm to reach the consensus on truthful results while avoiding expensive on-chain iterative processes. The participant selection scheme and reward policy are further utilized to filter appropriate participants to complete the task. Our scheme also protects data privacy and does not require communications between participants. Finally, we implement and deploy the contracts on Ethereum and conduct extensive experiments to demonstrate that the contracts can practically execute crowdsensing tasks.

源语言英语
页(从-至)974-986
页数13
期刊IEEE Internet of Things Journal
11
1
DOI
出版状态已出版 - 1 1月 2024

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