Quantifying the Utility-Privacy Tradeoff in the Smart Grid
Quantifying the Utility-Privacy Tradeoff in the Smart Grid
复制标题
量化智能电网中的公用事业与隐私权衡
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Sastry
中科院分区：
文献类型：
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作者：
Roy Dong;A. Cárdenas;L. Ratliff;Henrik Ohlsson;S. Sastry
电网的现代化和智能电表的安装带来了许多控制和监测的优势。然而，如果落入坏人之手，这些数据可能会构成隐私威胁。在本文中，我们考虑了智能电网运行与消费者隐私之间的权衡。通过考虑使用恒温控制负载的现实直接负载控制示例，我们分析了智能电网运行与收集数据频率之间的权衡，并给出了仿真结果，以显示其性能如何随着采样频率的降低而下降。此外，我们引入了一个新的隐私度量，我们称之为推理隐私。这个隐私度量假设了一个强大的攻击者模型，并提供了攻击者推断私有参数的能力的上限，与他使用的算法无关。结合这两个结果，我们可以直接考虑更好的负载控制和消费者隐私之间的权衡。
The modernization of the electrical grid and the installation of smart meters come with many advantages to control and monitoring. However, in the wrong hands, the data might pose a privacy threat. In this paper, we consider the tradeoff between smart grid operations and the privacy of consumers. We analyze the tradeoff between smart grid operations and how often data is collected by considering a realistic direct-load control example using thermostatically controlled loads, and we give simulation results to show how its performance degrades as the sampling frequency decreases. Additionally, we introduce a new privacy metric, which we call inferential privacy. This privacy metric assumes a strong adversary model, and provides an upper bound on the adversary's ability to infer a private parameter, independent of the algorithm he uses. Combining these two results allow us to directly consider the tradeoff between better load control and consumer privacy.