文章基本信息

标题：Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption
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作者：Dabeeruddin Syed ; Shady S. Refaat ; Othmane Bouhali 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：7
页码：357-373
DOI：10.3390/info11070357
出版社：MDPI Publishing
摘要：Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.
关键词：deep learning; homomorphic encryption; fault localization; smart grids; deep neural networks deep learning ; homomorphic encryption ; fault localization ; smart grids ; deep neural networks