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Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Author

Listed:
  • Wang Ren

    (Sichuan University
    China Information Technology Security Evaluation Center)

  • Xin Tong

    (China Information Technology Security Evaluation Center)

  • Jing Du

    (China Information Technology Security Evaluation Center)

  • Na Wang

    (China Information Technology Security Evaluation Center)

  • Shancang Li

    (University of the West of England)

  • Geyong Min

    (University of Exeter)

  • Zhiwei Zhao

    (University of Electronic Science and Technology of China)

Abstract

The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

Suggested Citation

  • Wang Ren & Xin Tong & Jing Du & Na Wang & Shancang Li & Geyong Min & Zhiwei Zhao, 2024. "Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation," Information Systems Frontiers, Springer, vol. 26(6), pages 2227-2238, December.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:6:d:10.1007_s10796-021-10116-w
    DOI: 10.1007/s10796-021-10116-w
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