论文标题
在智能建筑物中界限隐私泄漏
Bounding Privacy Leakage in Smart Buildings
论文作者
论文摘要
智能建筑管理系统依靠传感器来优化建筑物的运行。如果未经授权的用户可以访问这些传感器，则可能会发生隐私泄漏。本文考虑了智能住宅建筑中隐私的潜在泄漏，以及如何通过用添加剂高斯噪声破坏测量结果来减轻它。为了隐藏公寓的占用变化而进行这种腐败。任何估计变化时间的估计器的差异的下限。然后，该界限用于分析不同模型参数如何影响方差。结果表明，信号与噪声比和系统动力学是影响界限的主要因素。然后在KTH Live-In-In-In-In-In-In-In-In-In-In-In-In-In-In-Migulator上验证这些结果，显示出与理论结果的良好对应关系。
Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart residential building, and how it may be mitigated through corrupting the measurements with additive Gaussian noise. This corruption is done in order to hide the occupancy change in an apartment. A lower bound on the variance of any estimator that estimates the change time is derived. The bound is then used to analyze how different model parameters affect the variance. It is shown that the signal to noise ratio and the system dynamics are the main factors that affect the bound. These results are then verified on a simulator of the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.
