| 摘要: |
| 智能电网通过整合先进的信息与通信技术，将传统电网升级为更高效、可靠和可持续的系统。然而，集中化的数据处理模式面临着隐私泄露和数据安全挑战。为此，提出了一种面向智能电网的可验证隐私保护联邦学习方法，基于秘密共享及其同态性设计了一种安全数据聚合方案。在上传梯度信息前，用户在本地对梯度添加掩码并通过秘密共享与其他用户共享掩码值。服务器在收到经过隐私处理的梯度后利用同态特性恢复掩码之和，从而确保安全聚合并降低通信开销。在数据传输过程中，基于可验证秘密共享和认证加密技术进行了数据完整性验证，以确保客户端与服务器间传输数据的真实性和完整性。仿真结果表明，该方案在保证用户隐私和数据完整性的前提下仍具有优越的模型性能和较低的通信成本，在MNIST和CIFAR数据集上模型精度分别达到99.2%和99.5%。 |
| 关键词: 智能电网 隐私保护 联邦学习 可验证秘密共享 数据聚合 |
| DOI：10.20079/j.issn.1001-893x.250122005 |
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| 基金项目:广西电网公司科技项目（046100KC23040002） |
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| A Verifiable Privacy-preserving Federated Learning Method for Smart Grids |
| AI Xuhua,YIN Yuan,DONG Yun,TAN Qiwen,WEI Zonghui,HUANG Yiting |
| (China Southern Power Grid Guangxi Power Grid Co.,Ltd.,Nanning 530023,China) |
| Abstract: |
| The smart grid upgrades the traditional power system into a more efficient,reliable,and sustainable one by integrating advanced information and communication technologies.However,centralized data processing models face challenges related to privacy breaches and data security is designed.Therefore,a verifiable privacy-preserving federated learning method is proposed for the smart grid and a secure data aggregation scheme based on secret sharing and its homomorphic properties is designed.Before uploading gradient information,users locally add masks to their gradients and share the mask values with other users through secret sharing.Upon receiving the privacy-processed gradients,the server utilizes the homomorphic properties to recover the sum of the masks,ensuring secure aggregation and reducing communication overhead.Additionally,during data transmission,data integrity verification is conducted based on verifiable secret sharing and authenticated encryption techniques to ensure the authenticity and completeness of data transmitted between the client and the server.Simulation results demonstrate that this scheme maintains superior model performance and lower communication costs while ensuring user privacy and data integrity,achieving model accuracies of 99.2% and 99.5% on the MNIST and CIFAR datasets,respectively. |
| Key words: smart grid privacy protection federated learning verifiable secret sharing data aggregation |
《电讯技术》编辑部 