差别隐私
智能电网
计算机科学
架空(工程)
电
信息隐私
计算机安全
方案(数学)
网格
数据聚合器
计算机网络
数据挖掘
无线传感器网络
工程类
数学分析
几何学
数学
电气工程
操作系统
作者
Zhiqiang Zhao,Gao Liu,Yining Liu
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-1
标识
DOI:10.1109/tsg.2023.3349280
摘要
The detection of electricity theft, which focuses on privacy preservation and system security, has been extensively researched in the smart grid. However, existing solutions have not taken into account the enormous communication overhead that will be incurred in practical environments due to the large scale of the smart grid and the vast number of smart meters. Furthermore, most schemes have limited functionalities. There is also a lack of further research on the detection model and period. Therefore, we propose a practical privacy-preserving electricity theft detection scheme. Specifically, we inject the gamma noise into a user’s power consumption data to preserve user privacy without adversely affecting the accuracy of detection. Secondly, our approach achieves privacy-preserving dynamic billing and differential privacy for regional power consumption aggregation without requiring any additional operations. Additionally, we propose a novel combination detection model that extracts local and global features of data, and explore the impact of the detection period. Ultimately, we conduct a number of experiments based on real power consumption data and practical devices, and experimental results imply that the proposed scheme can operate on resource-constrained devices with lower communication overhead and better detection model performance compared with sate-of-the-art schemes.
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