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Privacy Protection for Data-Driven Smart Manufacturing Systems

Author

Listed:
  • Kok-Seng Wong

    (School of Software, Soongsil University, Seoul, South Korea)

  • Myung Ho Kim

    (School of Software, Soongsil University, Seoul, South Korea)

Abstract

The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. The transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets. The loss of sensitive information can lead to significant business loss and cause reputational damage. In this paper, the authors discuss emerging issues that are related to IIoT data sharing, investigate possible technological solutions to hide sensitive information and discuss some privacy management techniques in smart manufacturing systems.

Suggested Citation

  • Kok-Seng Wong & Myung Ho Kim, 2017. "Privacy Protection for Data-Driven Smart Manufacturing Systems," International Journal of Web Services Research (IJWSR), IGI Global, vol. 14(3), pages 17-32, July.
    • Handle: RePEc:igg:jwsr00:v:14:y:2017:i:3:p:17-32
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.2017070102
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    Cited by:

    1. Kamble, Sachin S. & Gunasekaran, Angappa & Parekh, Harsh & Joshi, Sudhanshu, 2019. "Modeling the internet of things adoption barriers in food retail supply chains," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 154-168.

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