A Model Randomization Approach to Statistical Parameter Privacy

In this article, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a non...

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Vydané v:	IEEE transactions on automatic control Ročník 68; číslo 2; s. 839 - 850
Hlavní autori:	Nekouei, Ehsan, Sandberg, Henrik, Skoglund, Mikael, Johansson, Karl Henrik
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Bandpass filters Building automation Building management systems Complexity theory Convex optimization Convexity Data privacy Filter design (mathematics) Filter designs Filter output Information theory Joint probability density function Kalman filtering Kalman filters Kernel Markov processes Mathematical transformations Mutual information Mutual informations Non-linear transformations Numerical prediction Optimization Parameter estimation Parameters Privacy privacy in networked control systems Probability density function Probability density functions Randomisation State estimation Statistical analysis Statistical parameters Structural design Testing Transformations (mathematics)
ISSN:	0018-9286, 1558-2523, 1558-2523
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