Towards a privacy-preserving smart contract-based data aggregation and quality-driven incentive mechanism for mobile crowdsensing

【Author】 Yu, Ruiyun; Oguti, Ann Move; Ochora, Dennis Reagan; Li, Shuchen

【Source】JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

【影响因子】7.574

【Abstract】The crowd's power, combined with the sensing capabilities of smart mobile de-vices, has resulted in the emergence of a revolutionary data acquisition paradigm known as Mobile Crowdsensing. In exchange for re-wards, mobile users collect and share location-specific data values. However, most existing crowdsensing systems built on traditional centralized architectures are highly prone to attacks, intrusions, single point of failure, manipulations, and low reliability. Recently, decentralized blockchain technologies are being applied in mobile crowdsensing systems to ensure workers' privacy, data privacy, and the quality of sensed data at a low service fee. By leveraging blockchain technology, this paper inherits the advantages of the public blockchain without the need for any trusted third-parties. We propose a smart contract-based privacy-preserving data aggregation and quality assessment protocol to infer reliable aggregated results and estimate data quality, wherein, we design a fair quality-driven incentive mechanism to distribute rewards based on the data quality. The protocol ensures a secure, cost-optimal, and reliable aggregation and estimation of the sensed data quality on the public blockchain without disclosing the sensed data's and participants' privacy. We adopt Interplanetary File Systems to offset the blockchain's expensive storage costs. Experiments were conducted using real-world datasets which were implemented on a full-stack on-chain and off-chain decentralized application on the Ethereum blockchain. The experimental results demonstrate our design is highly efficient in achieving privacy-preserving data aggregation and significantly reduces on-chain computation costs.

【Keywords】Mobile crowdsensing; Smart contracts; Data aggregation; Incentive mechanism; dApp; IPFS

【发表时间】2022 NOV

【收录时间】2022-09-15

【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