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Open AccessArticle

Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain

School of Software Engineering, South China University of Technology, Guangzhou 510006, China
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Author to whom correspondence should be addressed.
Academic Editor: Fatos Xhafa
Sensors 2021, 21(4), 1540; https://doi.org/10.3390/s21041540
Received: 25 January 2021 / Revised: 16 February 2021 / Accepted: 19 February 2021 / Published: 23 February 2021
(This article belongs to the Section Internet of Things)
The development of information technology has brought great convenience to our lives, but at the same time, the unfairness and privacy issues brought about by traditional centralized systems cannot be ignored. Blockchain is a peer-to-peer and decentralized ledger technology that has the characteristics of transparency, consistency, traceability and fairness, but it reveals private information in some scenarios. Secure multi-party computation (MPC) guarantees enhanced privacy and correctness, so many researchers have been trying to combine secure MPC with blockchain to deal with privacy and trust issues. In this paper, we used homomorphic encryption, secret sharing and zero-knowledge proofs to construct a publicly verifiable secure MPC protocol consisting of two parts—an on-chain computation phase and an off-chain preprocessing phase—and we integrated the protocol as part of the chaincode in Hyperledger Fabric to protect the privacy of transaction data. Experiments showed that our solution performed well on a permissioned blockchain. Most of the time taken to complete the protocol was spent on communication, so the performance has a great deal of room to grow. View Full-Text
Keywords: privacy; secure multi-party computation; permissioned blockchain; Hyperledger Fabric privacy; secure multi-party computation; permissioned blockchain; Hyperledger Fabric
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MDPI and ACS Style

Zhou, J.; Feng, Y.; Wang, Z.; Guo, D. Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain. Sensors 2021, 21, 1540. https://doi.org/10.3390/s21041540

AMA Style

Zhou J, Feng Y, Wang Z, Guo D. Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain. Sensors. 2021; 21(4):1540. https://doi.org/10.3390/s21041540

Chicago/Turabian Style

Zhou, Jiapeng; Feng, Yuxiang; Wang, Zhenyu; Guo, Danyi. 2021. "Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain" Sensors 21, no. 4: 1540. https://doi.org/10.3390/s21041540

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