电子DPNCT:一个增强的攻击弹性差异性隐私模型用于智能电网,使用分割噪声取消.
Khadija Hafeez1, Donna O'Shea2, Thomas Newe3
1Munster Technological University (MTU), Cork, Ireland. khadija.hafeez@mycit.ie.
Scientific reports
|November 9, 2023
概括
智能电网能源数据隐私受到勾结攻击的威胁. 我们推出了增强的差分私人噪声消除模型 (E-DPNCT),提供防和准确的负载监控,以增强消费者隐私.
科学领域:
- 网络安全 网络安全
- 智能电网技术 智能电网技术
- 数据 隐私 数据 隐私 数据
背景情况:
- 智能电网中的高频能源消耗数据揭示了敏感的消费者生活方式信息,造成了重大安全和隐私风险.
- 现有的智能电网差异隐私 (DP) 模型,虽然有助于计费和负载监控的分析,但容易受到对手的勾结攻击.
- 这些攻击利用恶意智能电表和不可信的聚合器之间的协作来提取私人数据.
研究的目的:
- 为了证明当前基于DP的隐私模型在智能电网中的脆弱性,以防止勾结攻击.
- 为智能电网数据提出一种新的,防的隐私模式.
- 确保准确的负载监控和计费,同时保护敏感的消费者信息.
主要方法:
- 这项研究首先通过分析DP模型的漏洞来确定需要一种防勾结模型的需求.
- 提出了一个新的模型,即智能电表 (E-DPNCT) 负载监控和计费的增强差分私有噪声取消模型.
- 电子DPNCT利用差异性隐私与分割噪声取消协议,涉及多个主智能仪表 (MSM) 进行防撞.
主要成果:
- 拟议的E-DPNCT模型证明了对勾结攻击的抵抗力,这与现有模型相比是显著的改进.
- 使用实时数据的模拟显示,在攻击场景下,隐私保护的实质性增强.
- 与EPIC等最先进的模型进行比较,证实了E-DPNCT对勾结攻击的有效性.
结论:
- 电子DPNCT模型有效地保护智能电网数据隐私免受共谋攻击.
- 它提供准确的负载监控和计费服务,同时提供强大的隐私保护.
- 该模型为增强智能电网数据分析中的安全性和隐私提供了可行的解决方案.
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