Blockchain-Enabled Multi-Party Computation for Privacy Preserving and Public Audit in Industrial IoT

Yuhan Yang, Jing Wu, Chengnian Long, Wei Liang, Yi-Bing Lin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

With the rapid increase of the industrial data and the development of the industrial Internet of Things paradigm, the efficiency and the quality of service of the emerging applications have been improved. However, the contradiction between data sharing and privacy preserving is still an obstacle in the industrial Internet of Things. To this end, we propose a privacy-preserving and publicly auditable multi-party computation scheme for industrial data sharing and computing, which avoids privacy leakage and computation misbehavior by separating the data ownership, data use and data verification. Using the blockchain technology, a transparent management platform is provided to recognize and trace the illegal data and computation behavior. Moreover, we integrate the non-interactive zero-knowledge proof in the multi-party interaction mechanism, wherein the verification of data consistency and computation validity is executed publicly on the blockchain. Finally, we implement experiment to evaluate the performance of the computation latency, communication overhead and the influence of encryption parameter, and the numerical results illustrate the efficiency and feasibility of our scheme.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
DOIs
StatePublished - Dec 2022

Keywords

  • Data privacy
  • blockchains
  • multi-party computation
  • Task analysis
  • Industrial Internet of things
  • Informatics
  • Security

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