Open Access

ARTICLE

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

by Linghong Kuang^1,2, Wenlong Shi^1,2, Jing Zhang^1,2,*

1 School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China
2 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou, 350118, China

* Corresponding Author: Jing Zhang. Email:

(This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)

Computers, Materials & Continua 2024, 80(2), 3193-3219. https://doi.org/10.32604/cmc.2024.054377

Received 26 May 2024; Accepted 18 July 2024; Issue published 15 August 2024

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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Keywords

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