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Data privacy-preserving distributed knowledge discovery based on the blockchain

Author

Listed:
  • Keon Myung Lee

    (Chungbuk National University)

  • Ilkyeun Ra

    (University of Colarado Denver)

Abstract

Data are collected and regarded as valuable assets in many business domains. Their owner would not want to disclose them to the public due to their potential value. Distributed knowledge discovery techniques have been proposed which assume the cooperation of data owners even though they might not behave in a trustworthy manner. When a party decides to quit the cooperation in the distributed knowledge discovery, the other parties cannot continue the discovery task and hence they get some disadvantage due to the party’s betrayal. This paper is concerned with data privacy-preserving distributed knowledge discovery which gives penalty to the party who quits the cooperation in the discovery process. It proposes a blockchain-based distributed machine learning method which does not disclose the participating parties’ data and gives the penalty to betraying parties. The proposed method makes the participating parties communicate with each other via the smart contract on the blockchain network. It uses a blockchain-based incentive system to establish trust among parties and to improve the quality of discovery knowledge. The proposed method has been implemented with a smart contract on the blockchain and tested for a benchmark data.

Suggested Citation

  • Keon Myung Lee & Ilkyeun Ra, 2020. "Data privacy-preserving distributed knowledge discovery based on the blockchain," Information Technology and Management, Springer, vol. 21(4), pages 191-204, December.
    • Handle: RePEc:spr:infotm:v:21:y:2020:i:4:d:10.1007_s10799-020-00317-1
    DOI: 10.1007/s10799-020-00317-1
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    References listed on IDEAS

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    1. Kyungyong Chung & Raouf Boutaba & Salim Hariri, 2016. "Knowledge based decision support system," Information Technology and Management, Springer, vol. 17(1), pages 1-3, March.
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    Cited by:

    1. Fu-Hsiang Chen & Ming-Fu Hsu & Kuang-Hua Hu, 2022. "Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model," Information Technology and Management, Springer, vol. 23(3), pages 213-231, September.

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