Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon Data Sharing in Power Systems under Carbon Neutrality

Driven by global carbon neutrality goals, carbon data sharing in power systems has become a critical component of low-carbon transformation. However, the high sensitivity and privacy requirements of carbon data pose significant challenges to traditional data security technologies. To address these challenges, this paper proposes an innovative lattice-based attribute encryption scheme designed to enhance the security and privacy of carbon data sharing in power systems. The scheme integrates the quantum-resistant properties of lattice-based cryptography with the fine-grained access control mechanisms of attribute-based encryption, enabling efficient encryption and secure sharing of carbon data. Through theoretical analysis, this paper demonstrates the scheme's superiority in resisting quantum computing attacks and supporting dynamic access policies. Experimental results indicate that the proposed scheme outperforms existing methods in terms of encryption efficiency (approximately 30% improvement), decryption success rate (over 99.5%), and resistance to attacks (withstanding 99.9% of simulated attacks). Furthermore, in large-scale power system environments, the scheme exhibits excellent scalability, supporting concurrent data sharing for over 10,000 nodes with a system latency of less than 200 milliseconds. This research not only provides an efficient and secure solution for carbon data sharing in power systems but also offers new insights into the application of lattice-based cryptography in real-world industrial scenarios, with significant theoretical and practical implications.

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Published
2025-05-09
DOI
https://doi.org/10.1109/bigdatasecurity66063.2025.00025
Primary Topic
Big Data and Digital Economy
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article
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article

Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon Data Sharing in Power Systems under Carbon Neutrality

Qianting Zhu, Wenjing Xu, Limei Zhang
Big Data and Digital Economy
article

Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon Data Sharing in Power Systems under Carbon Neutrality

Qianting Zhu, Wenjing Xu, Limei Zhang
article en

Abstract

Driven by global carbon neutrality goals, carbon data sharing in power systems has become a critical component of low-carbon transformation. However, the high sensitivity and privacy requirements of carbon data pose significant challenges to traditional data security technologies. To address these challenges, this paper proposes an innovative lattice-based attribute encryption scheme designed to enhance the security and privacy of carbon data sharing in power systems. The scheme integrates the quantum-resistant properties of lattice-based cryptography with the fine-grained access control mechanisms of attribute-based encryption, enabling efficient encryption and secure sharing of carbon data. Through theoretical analysis, this paper demonstrates the scheme's superiority in resisting quantum computing attacks and supporting dynamic access policies. Experimental results indicate that the proposed scheme outperforms existing methods in terms of encryption efficiency (approximately 30% improvement), decryption success rate (over 99.5%), and resistance to attacks (withstanding 99.9% of simulated attacks). Furthermore, in large-scale power system environments, the scheme exhibits excellent scalability, supporting concurrent data sharing for over 10,000 nodes with a system latency of less than 200 milliseconds. This research not only provides an efficient and secure solution for carbon data sharing in power systems but also offers new insights into the application of lattice-based cryptography in real-world industrial scenarios, with significant theoretical and practical implications.

China University of Petroleum, Beijing (CN)
Openalex Percentile: Top 85%
Big Data and Digital Economy
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Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon Data Sharing in Power Systems under Carbon Neutrality — Qianting Zhu, Wenjing Xu, et al. · (2025) | TGRS Research Map | TGRS