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一种面向智能电网的数据聚合隐私保护方案
作者:
作者单位:

1.国网四川省电力公司电力科学研究院;2.重庆大学大数据与软件学院

中图分类号:

TP309

基金项目:

国网四川省电力公司科技项目(SGSCDK00LYJS2200130)。


A novel privacy-preserving data aggregation scheme for smart grids
Author:
Affiliation:

1.Electric Power Research Institute of State Grid Sichuan Electric Power Company;2.School of Big Data Software Engineering,Chongqing University,Chongqing,,China

Fund Project:

State Grid Sichuan power company science and technology project(SGSCDK00LYJS2200130)

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    摘要:

    数据聚合是智能电网通信中的一项关键技术，能够以高效节能的方式收集用户用电数据。不过，随着智能电表的大规模部署，这无疑给用户带来了许多隐私问题，比如感知他们的生活习惯。基于此，本文提出了一种高效且保护隐私的数据聚合方案(Efficient and Privacy-Preserving Data Aggregation，EPPDA)。首先，一个基于区块链的智能电网四层架构被提出以支持用电电力数据聚合。在架构中的采集层中，改进了基础的Boneh-Goh-Nissim加密系统，使其更适合于电网隐私保护场景。在架构的平台层，利用区块链的防篡改特性对聚合数据进行有效的存储及查询。性能分析结果表明：提出的EPPDA可以满足智能电网系统的几种隐私特性。最后，通过实验数据表明，EPPDA在保证数据隐私和安全的条件下降低了计算与通信成本，提高了整个方案的效率。

    Abstract:

    Data aggregation is a key technology in smart grid communication for collecting the most critical data in an energy-efficient manner. However, with the large-scale deployment of smart meters, this undoubtedly brings many privacy concerns to users, such as sensing their lifestyle habits. Therefore, in this paper, we propose an efficient and privacy-preserving data aggregation (EPPDA) for IoT-enabled smart grid based on smart contracts. Firstly, we propose a four-layer architecture of blockchain-based smart grid communication to support data aggregation. At the collection layer in the architecture, the underlying Boneh-Goh-Nissim system is improved to make it more suitable for grid privacy protection scenarios. In the platform layer of the architecture, the tamper-proof feature of blockchain is utilized for efficient storage and query of aggregated data. The performance analysis results indicate that the proposed EPPDA can meet several privacy characteristics of smart grid systems. Finally, experimental data shows that the proposed EPPDA reduces the computation and communication costs and improves the efficiency of the whole scheme.

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  • 收稿日期:2023-06-07
  • 最后修改日期:2023-07-13
  • 录用日期:2023-07-17
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