A synchronous compression and encryption method for massive electricity consumption data privacy preserving

The demand for fine-grained perception of electricity usage information in the new power system is continuously increasing, making it a challenge to address potential unauthorized data access while ensuring channel security. This paper addresses privacy in power systems requiring efficient source-lo...

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Main Authors: Ruifeng Zhao, Jiangang Lu, Zhiwen Yu, Kaiwen Zeng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Energy Research
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2024.1444505/full
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author Ruifeng Zhao
Jiangang Lu
Zhiwen Yu
Kaiwen Zeng
author_facet Ruifeng Zhao
Jiangang Lu
Zhiwen Yu
Kaiwen Zeng
author_sort Ruifeng Zhao
collection DOAJ
description The demand for fine-grained perception of electricity usage information in the new power system is continuously increasing, making it a challenge to address potential unauthorized data access while ensuring channel security. This paper addresses privacy in power systems requiring efficient source-load interactions by introducing a novel data compression synchronous encryption algorithm within a compressed sensing framework. Our proposed algorithm uses a ternary Logistic-Tent chaotic system for generating a chaotic measurement matrix, allowing simultaneous data compression and encryption of user-side voltage and current data. This mitigates high-frequency sampling overload and ensures data confidentiality. The implementation of a joint random model at both compression and reconstruction stages eliminates the need for key transmission, reducing management costs and leakage risks. The proposed algorithm was validated using the PLAID dataset, demonstrating that the time required for a single encryption-decryption operation can be reduced by up to 81.99% compared to the asymmetric RSA algorithm. Additionally, compared to the symmetric AES algorithm, the proposed method significantly enhances confidentiality.
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institution Kabale University
issn 2296-598X
language English
publishDate 2025-01-01
publisher Frontiers Media S.A.
record_format Article
series Frontiers in Energy Research
spelling doaj-art-8fb4e94600594bf0a1e474f5919b37b72025-01-03T06:47:19ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.14445051444505A synchronous compression and encryption method for massive electricity consumption data privacy preservingRuifeng ZhaoJiangang LuZhiwen YuKaiwen ZengThe demand for fine-grained perception of electricity usage information in the new power system is continuously increasing, making it a challenge to address potential unauthorized data access while ensuring channel security. This paper addresses privacy in power systems requiring efficient source-load interactions by introducing a novel data compression synchronous encryption algorithm within a compressed sensing framework. Our proposed algorithm uses a ternary Logistic-Tent chaotic system for generating a chaotic measurement matrix, allowing simultaneous data compression and encryption of user-side voltage and current data. This mitigates high-frequency sampling overload and ensures data confidentiality. The implementation of a joint random model at both compression and reconstruction stages eliminates the need for key transmission, reducing management costs and leakage risks. The proposed algorithm was validated using the PLAID dataset, demonstrating that the time required for a single encryption-decryption operation can be reduced by up to 81.99% compared to the asymmetric RSA algorithm. Additionally, compared to the symmetric AES algorithm, the proposed method significantly enhances confidentiality.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1444505/fullelectricity consumption informationprivacy preservingcompressed sensingjoint random modelchaotic system
spellingShingle Ruifeng Zhao
Jiangang Lu
Zhiwen Yu
Kaiwen Zeng
A synchronous compression and encryption method for massive electricity consumption data privacy preserving
Frontiers in Energy Research
electricity consumption information
privacy preserving
compressed sensing
joint random model
chaotic system
title A synchronous compression and encryption method for massive electricity consumption data privacy preserving
title_full A synchronous compression and encryption method for massive electricity consumption data privacy preserving
title_fullStr A synchronous compression and encryption method for massive electricity consumption data privacy preserving
title_full_unstemmed A synchronous compression and encryption method for massive electricity consumption data privacy preserving
title_short A synchronous compression and encryption method for massive electricity consumption data privacy preserving
title_sort synchronous compression and encryption method for massive electricity consumption data privacy preserving
topic electricity consumption information
privacy preserving
compressed sensing
joint random model
chaotic system
url https://www.frontiersin.org/articles/10.3389/fenrg.2024.1444505/full
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