A Model Randomization Approach to Statistical Parameter Privacy

• Published in: IEEE transactions on automatic control Vol. 68; no. 2; pp. 839 - 850
• Main Authors: Nekouei, Ehsan, Sandberg, Henrik, Skoglund, Mikael, Johansson, Karl Henrik
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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)

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...