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FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing

Author

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  • Yuling Chen

    (State Laboratory of Public Big Data, GuiZhou University, Guizhou 550025, China
    College of Computer Science and Technology, GuiZhou University, Guizhou 550025, China)

  • Jinyi Guo

    (School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China)

  • Changlou Li

    (School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China)

  • Wei Ren

    (State Laboratory of Public Big Data, GuiZhou University, Guizhou 550025, China
    School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
    Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences (Wuhan), Wuhan 430074, China)

Abstract

In the big data era, data are envisioned as critical resources with various values, e.g., business intelligence, management efficiency, and financial evaluations. Data sharing is always mandatory for value exchanges and profit promotion. Currently, certain big data markets have been created for facilitating data dissemination and coordinating data transaction, but we have to assume that such centralized management of data sharing must be trustworthy for data privacy and sharing fairness, which very likely imposes limitations such as joining admission, sharing efficiency, and extra costly commissions. To avoid these weaknesses, in this paper, we propose a blockchain-based fair data exchange scheme, called FaDe. FaDe can enable de-centralized data sharing in an autonomous manner, especially guaranteeing trade fairness, sharing efficiency, data privacy, and exchanging automation. A fairness protocol based on bit commitment is proposed. An algorithm based on blockchain script architecture for a smart contract, e.g., by a bitcoin virtual machine, is also proposed and implemented. Extensive analysis justifies that the proposed scheme can guarantee data exchanging without a trusted third party fairly, efficiently, and automatically.

Suggested Citation

  • Yuling Chen & Jinyi Guo & Changlou Li & Wei Ren, 2019. "FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing," Future Internet, MDPI, vol. 11(11), pages 1-13, October.
  • Handle: RePEc:gam:jftint:v:11:y:2019:i:11:p:225-:d:279938
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    Cited by:

    1. Nathalie Jorzik & Paula Johanna Kirchhof & Frank Mueller-Langer, 2024. "Industrial data sharing and data readiness: a law and economics perspective," European Journal of Law and Economics, Springer, vol. 57(1), pages 181-205, April.

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