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Novel Temporal Perturbation-Based Privacy-Preserving Mechanism for Smart Meters

  • Xiaoyan Wang
  • Zhenquan XuEmail author
  • Ziwei Cai
  • Tao Wang
Article
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Abstract

Smart meters provide strong data support for the intelligent construction of power grids but expose users’ privacy-sensitive information to adversaries. Thus, several mechanisms have been proposed for smart meter privacy protection. However, these existing mechanisms either lack consideration of the protection for customer power consumption mode or focus only on data security and ignore the data availability for intelligent services of smart meters. In this study, we propose a temporal perturbation-based privacy-preserving mechanism to achieve a balance between privacy security and data availability. The time disturbance model improves privacy security by staggering the acquisition and release time of smart meter data, destructs the load characteristics hidden in the power waveform, and realizes the fuzzification of real consumption events. Moreover, data availability for metering and billing, electronic scheduling and management, and value-added services is guaranteed. We analyze data availability and privacy security from theoretical and experimental perspectives, deduce a data availability analysis model, and introduce a non-intrusive load monitoring algorithm as a testing method for evaluating security performance. Evaluation results show that the proposed scheme performs well in protecting users’ consumption pattern and is resistant to attacks by load detection; it also ensures data availability when used for intelligent services.

Keywords

Smart meter Privacy protection Data availability Time disturbance Non-intrusive load monitoring 

Notes

Acknowledgements

The research work reported in this paper is supported by the National Natural Science Foundation of China (41671443, 61701453), Fundamental Research Funds for the Central Universities (2042017kf0044), and LIESMARS Special Research Funding.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiaoyan Wang
    • 1
    • 2
  • Zhenquan Xu
    • 1
    • 2
    Email author
  • Ziwei Cai
    • 1
    • 2
  • Tao Wang
    • 1
    • 2
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  2. 2.Collaborative Innovation Center for Geospatial TechnologyWuhanChina

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