Fair payments for privacy-preserving aggregation of mobile crowdsensing data

【Author】 Dorsala, Mallikarjun Reddy; Sastry, V. N.; Chapram, Sudhakar

【Source】JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES

【影响因子】8.839

【Abstract】With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environ-mental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability - The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments - The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain. (C) 2021 The Authors. Published by Elsevier B.V.

【Keywords】Blockchain; Privacy -preserving aggregation; Fair Payments; Mobile crowdsensing; Smart contracts; Cryptocurrency

【发表时间】2022 SEP

【收录时间】2022-10-27

【文献类型】实验仿真

【主题类别】

区块链技术-核心技术-隐私保护