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Description and Analysis of Data Security Based on Differential Privacy in Enterprise Power Systems

Author

Listed:
  • Zhaofeng Zhong

    (Hitachi Building Technology (Guangzhou) Co., Ltd., Guangzhou 510610, China
    These authors contributed equally to this work.)

  • Ge Zhang

    (The Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China
    These authors contributed equally to this work.)

  • Li Yin

    (The Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Yufeng Chen

    (The Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China)

Abstract

The pursuit of environmental sustainability, energy conservation, and emissions reduction has become a global focal point. Electricity is the primary source of energy in our daily lives. Through the analysis of smart power systems, we can efficiently and sustainably harness electrical energy. However, electric power system data inherently contain a wealth of sensitive user information. Therefore, our primary concern is protecting these sensitive user data while performing precise and effective analysis. To address this issue, we have innovatively proposed three granular information models based on differential privacy. In consideration of the characteristics of enterprise electricity consumption data and the imperative need for privacy protection, we implement a reasonable modeling process through data processing, information granulation expression, and the optimization analysis of information granularity. Our datasets encompass enterprise electricity consumption data and related attributes from Hitachi Building Technology (Guangzhou) Co., Ltd’s cloud computing center. Simultaneously, we have conducted experiments using publicly available datasets to underscore the model’s versatility. Our experimental results affirm that granular computation can improve the utility of differential privacy in safeguarding data privacy.

Suggested Citation

  • Zhaofeng Zhong & Ge Zhang & Li Yin & Yufeng Chen, 2023. "Description and Analysis of Data Security Based on Differential Privacy in Enterprise Power Systems," Mathematics, MDPI, vol. 11(23), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4829-:d:1291302
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    References listed on IDEAS

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    1. Pedrycz, Witold, 2014. "Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing," European Journal of Operational Research, Elsevier, vol. 232(1), pages 137-145.
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