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...
Saved in:
Main Authors: | , , , |
---|---|
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
_version_ | 1841561046456729600 |
---|---|
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. |
format | Article |
id | doaj-art-8fb4e94600594bf0a1e474f5919b37b7 |
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 |
work_keys_str_mv | AT ruifengzhao asynchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT jianganglu asynchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT zhiwenyu asynchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT kaiwenzeng asynchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT ruifengzhao synchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT jianganglu synchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT zhiwenyu synchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving AT kaiwenzeng synchronouscompressionandencryptionmethodformassiveelectricityconsumptiondataprivacypreserving |