数据驱动和保护隐私的风险评估方法基于智能电网的联合学习
Song Deng1, Longxiang Zhang2, Dong Yue3
1Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China. dengsong@njupt.edu.cn.
Communications engineering
|November 3, 2024
概括
本研究引入了一种新的联合学习框架,用于智能电网安全风险评估,增强运营规划和威胁检测,同时通过深度学习和加密保护数据隐私.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 智能电网产生了大量的高维数据,挑战了传统的风险评估方法.
- 智能电网中的集中风险评估引发了隐私问题和运营商不愿意共享数据.
- 现有的方法在评估安全风险时,难以应对数据量和隐私保护问题.
研究的目的:
- 为智能电网安全风险评估开发数据驱动,保护隐私的方法.
- 解决处理大型数据集和保护敏感信息的传统方法的局限性.
- 通过准确的私人风险评估,加强智能电网的运营规划和威胁检测.
主要方法:
- 实施了双层风险指标系统,并扩展了用于全面分析的数据集.
- 利用深度卷积神经网络 (CNN) 模型来分析系统变量和风险水平.
- 开发了一种安全的联合风险评估协议,用于参数保护,采用同型加密.
主要成果:
- 拟议的方法在IEEE 14-bus和IEEE 118-bus系统上的安全风险评估中显示出高准确性.
- 实验结果证实了联合学习方法在保护数据隐私方面的有效性.
- 深度学习和安全加密的整合在培训期间成功保护了模型参数.
结论:
- 新的联合学习框架有效地平衡了准确的智能电网风险评估与强大的数据隐私.
- 这种方法克服了传统方法中大型数据集和隐私问题所带来的挑战.
- 该方法提供了一种可靠的解决方案,用于安全的运营规划和智能电网中的威胁检测.
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