Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this article, we cast one-shot non-intru...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2021-05, Vol.12 (3), p.2529-2543
Main Authors: Wang, Haoxiang, Zhang, Jiasheng, Lu, Chenbei, Wu, Chenye
Format: Article
Language:English
Subjects:
Compressed sensing
compressive sensing
Differential privacy
Energy consumption
Inference algorithms
Meters
Monitoring
non-intrusive load monitoring
Parameters
Privacy
Sensitivity
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Summary:Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this article, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between the NILM inference accuracy and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multi-shot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.3038757