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- W4313349362 abstract "There is serious electricity theft in the smart grid, which will cause huge economic losses to the power supplier. Therefore, it is very important to conduct electricity theft detection. Using machine/deep learning-based approaches to conduct electricity theft detection can greatly save the time and cost of manual investigation. Existing machine learning-based electricity theft detection schemes often treat the detector as trusted and directly publish user electricity consumption data to the detector for detection. However, there are generally no trusted detectors in reality, so these electricity theft detection schemes have the problem of leaking user data privacy. In this paper, we propose a privacy-preserving electricity theft detection (PETD) scheme, which adds noise to user electricity consumption data, and then publishes the noise-added electricity consumption data to the untrusted detector for detection, thereby protecting the privacy of user data. Experiments have shown that after adding noise to user data, although the performance of our model has been reduced, it is still within an acceptable range, thus achieving a balance between data privacy and data availability." @default.
- W4313349362 created "2023-01-06" @default.
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- W4313349362 date "2022-01-01" @default.
- W4313349362 modified "2023-09-27" @default.
- W4313349362 title "A Privacy-Preserving Electricity Theft Detection (PETD) Scheme for Smart Grid" @default.
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- W4313349362 doi "https://doi.org/10.1007/978-981-19-7769-5_4" @default.
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