Enhancing power data security: A blockchain and threshold cryptography approach

The digital transformation of power systems necessitates robust solutions for data security challenges, such as provenance verification, tamper resistance, and privacy compliance. This research proposes a novel blockchain-based architecture integrated with threshold proxy re-encryption (TPRE) to establish a trustworthy electricity data ecosystem. The framework transforms grid devices and organizational units into permissioned blockchain nodes, creating an immutable ledger for end-to-end data provenance while preventing unauthorized modifications. Regulatory entities gain granular traceability capabilities to audit data flows and pinpoint anomalous nodes with cryptographic certainty. To address the dual requirements of multi-party data sharing and privacy protection in smart grid environments, we implement an optimized TPRE scheme that significantly reduces computational overhead compared to conventional approaches. Our solution enables secure delegation of re-encryption rights while maintaining threshold-based access control over sensitive power consumption data. Experimental results demonstrate that the proposed scheme reduces computational overhead by 33.3% under multi-user scenarios compared to conventional proxy re-encryption methods, while enhancing key randomness through threshold-based secret sharing. The proposed framework offers practical insights for implementing cryptographically secure, regulator-compliant data management systems in critical energy infrastructure.

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Publication Details

Journal
Journal of Computational Methods in Sciences and Engineering
Published
2025-06-21
DOI
https://doi.org/10.1177/14727978251352144
Citations
1
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
3.25
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article

Enhancing power data security: A blockchain and threshold cryptography approach

Dong Jun, Xiaodong Zhou, Mi Zhou, Gu Haitong
1 citations
Journal of Computational Methods in Sciences and Engineering
Blockchain Technology Applications and Security
3.25
article

Enhancing power data security: A blockchain and threshold cryptography approach

Dong Jun, Xiaodong Zhou, Mi Zhou, Gu Haitong
article en
1 citations

Abstract

The digital transformation of power systems necessitates robust solutions for data security challenges, such as provenance verification, tamper resistance, and privacy compliance. This research proposes a novel blockchain-based architecture integrated with threshold proxy re-encryption (TPRE) to establish a trustworthy electricity data ecosystem. The framework transforms grid devices and organizational units into permissioned blockchain nodes, creating an immutable ledger for end-to-end data provenance while preventing unauthorized modifications. Regulatory entities gain granular traceability capabilities to audit data flows and pinpoint anomalous nodes with cryptographic certainty. To address the dual requirements of multi-party data sharing and privacy protection in smart grid environments, we implement an optimized TPRE scheme that significantly reduces computational overhead compared to conventional approaches. Our solution enables secure delegation of re-encryption rights while maintaining threshold-based access control over sensitive power consumption data. Experimental results demonstrate that the proposed scheme reduces computational overhead by 33.3% under multi-user scenarios compared to conventional proxy re-encryption methods, while enhancing key randomness through threshold-based secret sharing. The proposed framework offers practical insights for implementing cryptographically secure, regulator-compliant data management systems in critical energy infrastructure.

Journal of Computational Methods in Sciences and Engineering
China Southern Power Grid (China) (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Blockchain Technology Applications and Security
3.25
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Enhancing power data security: A blockchain and threshold cryptography approach — Dong Jun, Xiaodong Zhou, et al. · Journal of Computational Methods in Sciences and Engineering (2025) | TGRS Research Map | TGRS