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Security level protection for intelligent terminals based on differential privacy

Author

Listed:
  • Feng Wang

    (University of Electronic Science and Technology of China)

  • Dingde Jiang

    (University of Electronic Science and Technology of China)

  • Hong Wen

    (University of Electronic Science and Technology of China)

  • Sheng Qi

    (University of Electronic Science and Technology of China)

Abstract

In the Internet of Things architecture, the distributed management structure of the data center-micro data center (MDC)-intelligent terminals is the foundation for intelligent terminals accessing, real-time data interaction and data release. It is necessary to perform security level detection before intelligent terminals accessing to the MDCs, which can facilitate MDCs to understand the ability to resist malicious attacks and realize reasonable use of terminals in different security levels. However, the dataset of security levels will be stored in MDCs and transferred as terminals removed. Therefore, an expedite privacy protection method for this dataset is required. This paper studies the privacy protection schemes based on differential privacy (DP) protection and proposes a level-proportion-based differential privacy protection method, utilizing the security level and the level proportion of the intelligent terminals as the parameters to apply DP protection with different intensities, so that the statistical properties of the dataset will not be destroyed. Simulation results show that our method can discriminatively implement DP protection for intelligent terminals with different levels. Moreover, it can hold the statistical properties of the dataset for further utilization.

Suggested Citation

  • Feng Wang & Dingde Jiang & Hong Wen & Sheng Qi, 2020. "Security level protection for intelligent terminals based on differential privacy," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(4), pages 425-435, August.
    • Handle: RePEc:spr:telsys:v:74:y:2020:i:4:d:10.1007_s11235-020-00665-x
    DOI: 10.1007/s11235-020-00665-x
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    References listed on IDEAS

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    1. Dingde Jiang & Liuwei Huo & Ya Li, 2018. "Fine-granularity inference and estimations to network traffic for SDN," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-23, May.
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