Privacy-preserving method for sensitive partitions of electricity consumption data based on hybrid differential privacy and k-anonymity

With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user ele...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-07, Vol.2806 (1), p.12010
Main Authors: Zhai, Feng, Liang, Xiaobing, Qin, Yu, Li, Baofeng, Shen, Lixiang, Xie, Jingyi
Format: Article
Language:English
Subjects:
Electricity consumption
Privacy
Smart grid
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Summary:With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user electricity data in smart grid, we propose a privacy protection method for sensitive partition of electricity data based on hybrid differential privacy and k-anonymity technique, which identifies pattern-specific quasi-identifiers by random forest technique and divides the data into privacy-invasive and non-privacy-invasive partitions, and adopts the Laplacian noise addition (numerical quasi-identifiers) and data generalisation (sub-types of quasi-identifiers) techniques to achieve differential privacy protection for different partitions. Finally, it is experimentally verified that the method performs better in terms of information reduction accuracy and execution time, effectively striking a balance between protecting user privacy and maintaining data utility.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2806/1/012010