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Preprints, Working Papers, ... Year : 2020

Parameter Privacy versus Control Performance: Fisher Information Regularized Control

Abstract

This article introduces and solves a new privacy-related optimization problem for cyber-physical systems where an adversary tries to learn the system dynamics. In the context of linear quadratic systems, we consider the problem of achieving a small cost while balancing the need for keeping knowledge about the model's parameters private. To this end, we formulate a Fisher information regularized version of the linear quadratic regulator with cheap cost. Here the control operator is allowed to not only control the plant but also mask its state by injecting further noise. Within the class of linear policies with additive noise, we solve this problem and show that the optimal noise distribution is Gaussian with state dependent covariance. Next, we prove that the optimal linear feedback law is the same as without regularization. Finally, to motivate our proposed scheme, we formulate an equivalent minimax problem for the worst-case scenario in which the adversary has full knowledge of all other inputs and outputs. Here, our policies are minimax optimal with respect to maximizing the variance over all unbiased estimators.
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Dates and versions

hal-02318237 , version 1 (16-10-2019)
hal-02318237 , version 2 (16-03-2020)

Identifiers

  • HAL Id : hal-02318237 , version 2

Cite

Ingvar Ziemann, Henrik Sandberg. Parameter Privacy versus Control Performance: Fisher Information Regularized Control. 2020. ⟨hal-02318237v2⟩

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