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Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption

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submitted on 2024-05-16, 12:04 and posted on 2024-05-16, 12:24 authored by Dabeeruddin Syed, Shady S. Refaat, Othmane Bouhali

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.

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Open Access funding provided by the Qatar National Library.

Smart Grid Dynamic Control and Management with Big Data Process Platform -



  • English



Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU
  • Texas A&M University at Qatar
  • Texas A&M at Qatar Advanced Scientific Computing Center - TAMUQ

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