论文标题
线性高斯动力学系统混淆的基本限制：一种信息理论方法
Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems: An Information-Theoretic Approach
论文作者
论文摘要
在本文中，我们通过信息理论方法研究了线性高斯动力系统的隐私式折衷方案，研究了混淆的基本限制。特别是，当要将隐私面膜添加到动态系统的输出中时，我们获得了分析公式，可以捕获基本的隐私式权威权衡，同时表明如何以最佳方式设计隐私面具：隐私面具应基于系统和噪声属性，用电源构图为供电的高斯构图。
In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to the outputs of the dynamical systems, while indicating explicitly how to design the privacy masks in an optimal way: The privacy masks should be colored Gaussian with power spectra shaped specifically based upon the system and noise properties.
