Data Privacy Sharing Scheme Based on Proxy Re-encryption and Blockchain
The rapid development of smart grid has brought about a massive amount of personal and organizational privacy data. The storage and sharing of this data face the dual challenges of privacy protection and data silos. With the advancement of technologies such as machine learning and deep learning, the sharing of power data has become crucial for the diagnosis, optimization, and prediction of the power grid. To address these issues, a novel dual-server power data privacy sharing scheme is proposed, which leverages proxy re-encryption and blockchain technology. By encrypting power data and uploading it to a blockchain consortium specifically designed for energy companies, the scheme ensures the immutability and verifiability of the data. Upon receiving a user’s data sharing request, the energy utility encrypts the data into a second-level ciphertext using the proxy re-encryption encryption algorithm and subsequently uploads it to a cloud server. The cloud server then carries out homomorphic operations in accordance with the user’s request. To enable users to decrypt the ciphertext using their own private keys, the two servers collaborate to execute a secure ciphertext transformation protocol. This is followed by the application of the proxy re-encryption algorithm, which transforms the homo- morphically operated ciphertext into a format that aligns with the user’s public key. The experimental outcomes demonstrate that this proposed scheme not only facilitates the sharing of user power data in a multi-key environment but also incurs computational overhead that is remarkably similar to that of the BFV homomorphic encryption algorithm.
Authors
- Zhaoli Chen (ORCID: https://orcid.org/0000-0003-2253-7688)
- Yiting Huang
- Yuan Yin
- Yun Dong (ORCID: https://orcid.org/0000-0001-6909-7777)
- Xuhua Ai
- Liyuan Zhang (ORCID: https://orcid.org/0000-0002-6411-6526)
Institutions
- China Southern Power Grid (China) (CN)
Publication Details
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1109/scecs65243.2025.11065902
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48