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A secure and privacy-preserving data aggregation and classification model for smart grid

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Abstract

Smart meters are rapidly installing by utility providers to improve the reliability and performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor, predict, generate and distribute power. The customer’s real-time activity and power usage can be revealed by analyzing the smart meter data. Therefore, the security and privacy of the data is a crucial issue for the smart grid. This paper proposes a secure and privacy-preserving data aggregation and classification (SP-DAC) model based on fog and cloud architecture. Data is aggregated at the fog node in the SP-DAC model, and classification is performed at the outsourced cloud with three machine learning classifiers. Simulation results analyze the cryptographic costs and classification performance. Real-world smart meter dataset “UMass Smart” is taken for experiments and classification accuracy, precision, recall, and F1 score achieved upto 88%, 87%, 90%, and 88%, respectively. The comparison with existing models shows the superiority of the SP-DAC model in terms of features and parameters.

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Acknowledgements

The authors would like to thanks the National Institute of Technology, Kurukshetra, India for financially supporting the research work.

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  1. National Institute of Technology, Kurukshetra, India

    Ashutosh Kumar Singh & Jatinder Kumar

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  1. Ashutosh Kumar Singh
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  2. Jatinder Kumar
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Correspondence to Ashutosh Kumar Singh.

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Singh, A.K., Kumar, J. A secure and privacy-preserving data aggregation and classification model for smart grid. Multimed Tools Appl 82, 22997–23015 (2023). https://doi.org/10.1007/s11042-023-14599-4

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