.png){kind=icon}
返回
Optimal Privacy-Aware Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2021.3077868.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article studies the design of an optimal privacy-aware estimator of a public random variable based on noisy measurements, which contain private information. The public variable carries also nonprivate information, but its estimate will be correlated with the private information due to the estimation process. The objective is to design an optimal estimator of the public random variable such that the leakage of private information, via the estimation process, is kept below a certain level. The privacy metric is defined as the discrete conditional entropy of the private variable given the output of the estimator. We show that the optimal privacy-aware estimator is the solution of a (possibly infinite-dimensional) convex optimization problem when the estimator has access to either the measurement or the measurement together with the private information. We study the optimal perfect-privacy estimation problem that ensures the estimate of the public variable is independent of the private information. A necessary and sufficient condition is derived guaranteeing that an estimator satisfies the perfect-privacy requirement. It is shown that the optimal perfect-privacy estimator is the solution of a linear optimization problem. A sufficient condition for its existence is derived. The impact of the distribution mismatch on the perfect-privacy condition is studied. Numerical examples are used to illustrate the privacy-accuracy tradeoff.
Keyword:
Privacy
Random variables
Estimation
Noise measurement
Sensors
Temperature measurement
Optimization
Conditional entropy
information theory
networked control systems
privacy-aware estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容