基于联盟链的电力数据安全存储与访问控制方案

Consortium blockchain-based secure storage and access control of power data

  • 摘要: 随着电力行业数字化转型和智能电网技术的深入发展,电力系统中产生的大规模数据对安全存储与高效访问提出了更高要求。传统存储模式存在监管困难、溯源效率低等问题。为此,该文提出了一种基于联盟链的电力数据安全存储与访问控制方案。构建了一种链上链下相结合的电力数据存储模式,将电力数据加密后存储在存储服务器,并利用联盟链记录关键信息,确保了数据的机密性、不可篡改性和可追溯性。利用布隆过滤器和对称加密设计了一种支持关键字检索的加密存储方法,在保证数据安全存储的同时,实现了高效的数据检索,提升了系统的实用性。此外,基于智能合约和盲签名实现了基于用户属性的访问控制,实现细粒度的权限管理,确保数据访问的安全性和灵活性。同时提供了形式化的安全性分析,证明所提方案在保证关键字安全性的同时能抵抗冒充攻击和窃听攻击。性能分析表明,与现有方案相比,所提方案在数据搜索阶段将计算开销降低了68%,在数据访问阶段将通信开销降低了37%。

     

    Abstract: With the digital transformation of the power industry and the widespread adoption of smart grid technologies, the massive volume of data generated within power systems has imposed higher demands on secure storage and efficient access. However, traditional data storage models face challenges, such as difficulties in regulatory supervision and low efficiency in data traceability. To address these issues, this paper proposes a secure storage and access control scheme for power data based on consortium blockchains. In this scheme, a hybrid storage model combining on-chain and off-chain mechanisms is established, the encrypted power data is stored in the storage server, and the key information is recorded on the consortium blockchain, which ensures data confidentiality, immutability, and traceability. An encrypted data storage method supporting keyword search is designed by Bloom filters and symmetric encryption. While ensuring the secure storage of data, the designed method achieves efficient data retrieval and enhances the practicality of systems. Moreover, smart contract and blind signature are utilized to achieve user attribute-based access control, which achieves fine-grained permission management and ensures the security and flexibility of data access. A formal security analysis demonstrates that the proposed scheme can effectively resist impersonation and eavesdropping attacks while ensuring the security of keyword information. Performance evaluation shows that, compared with existing schemes, the proposed solution reduces computational overhead by 68% during the data search phase and lowers communication overhead by 37% during the data access phase.

     

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