实现网络安全和隐私增强的智能电网:一个区块链启用联合学习框架
Fatima Tariq1,2, Fatima Anjum1, Xiaochun Cheng3
1Department of Computer Science, Lahore College for Women University, Lahore, Punjab, Pakistan.
PloS one
|March 2, 2026
概括
本研究介绍了一种结合联合学习 (FL) 和差异隐私 (DP) 的新型架构,并采用双层区块链来增强智能电网安全性. 该框架提高了数据隐私和预测准确性,同时保持了用户匿名性和系统安全性.
科学领域:
- 智能电网技术 智能电网技术
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 智能电网提高了能源效率,但也带来了重大隐私和安全风险.
- 集中式机器学习和现有的联合学习系统与数据隐私和身份验证作斗争.
- 区块链解决方案往往缺乏强大的隐私机制和可追溯性.
研究的目的:
- 为智能电网开发安全和保护隐私的架构.
- 解决联合学习和基于区块链的系统中的漏洞.
- 提高能源消耗预测中的数据隐私,安全性和预测准确性.
主要方法:
- 一个双层区块链架构,与联合学习 (FL) 和中央差异隐私 (DP) 集成.
- 安全记录客户端交互,使用加盐的加密哈希来实现可追溯性和匿名性.
- 对中央DP的聚合模型参数添加高斯噪声,减轻推理攻击.
主要成果:
- 拟议的框架有效地提高了智能电网环境中的数据隐私和安全性.
- 随机森林模型在PRECON数据集上实现了高精度,MAE为0.153,MAPE为0.085和MSE为0.143.
- 该系统成功地保持了预测准确度,同时提高了隐私和安全性.
结论:
- 集成的双层区块链,FL和DP架构为智能电网隐私和安全挑战提供了强大的解决方案.
- 这种方法可以确保用户匿名性和数据完整性,而不会影响能源需求预测的性能.
- 该框架在智能电网系统中保护敏感消费者数据方面取得了重大改进.
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