Abstract
Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning‐based detection may expose user privacy. In this paper, we propose a real‐time fault‐tolerant and privacy‐preserving electricity theft detection (FPETD) scheme that combines n‐source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault‐tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.
Highlights
Electricity theft is widespread in the smart grid [1]; illegal users may be trying to reduce their bills by stealing electricity
(2) We propose a fault-tolerant n-source anonymity data collection scheme, so that users’ electricity consumption data can still be collected privately in the event some smart meters fail, thereby ensuring that electricity theft detection can still be performed normally in the case of device failure
We propose the fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme to realize realtime electricity theft detection in the smart grid
Summary
Electricity theft is widespread in the smart grid [1]; illegal users may be trying to reduce their bills by stealing electricity. The anonymized raw data collected by the n-source anonymity method can realize the detection of electricity theft under the premise of protecting user privacy. (1) We perform privacy processing on the users’ electricity consumption data by n-source anonymity before it is published, to complete real-time electricity theft detection without the need of a trusted third party while ensuring user privacy (2) We propose a fault-tolerant n-source anonymity data collection scheme, so that users’ electricity consumption data can still be collected privately in the event some smart meters fail, thereby ensuring that electricity theft detection can still be performed normally in the case of device failure (3) Sufficient experiments prove that the data normalization and dimensionality reduction preprocessing we do on the dataset can speed up the model training speed and improve the detection accuracy.
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