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Article | Open Access

Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

Linghong Kuang1,2Wenlong Shi1,2Jing Zhang1,2( )
School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou, 350118, China
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Abstract

The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing with hierarchical threshold encryption, offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid. The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance, thereby assigning appropriate protection levels. Furthermore, a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs, ensuring secure aggregation and encryption of user data. Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs, thereby safeguarding user data in the smart grid.

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Computers, Materials & Continua
Pages 3193-3219

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Cite this article:
Kuang L, Shi W, Zhang J. Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance. Computers, Materials & Continua, 2024, 80(2): 3193-3219. https://doi.org/10.32604/cmc.2024.054377

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Received: 26 May 2024
Accepted: 18 July 2024
Published: 15 August 2024
© The Author 2024.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.