Preserving State and Control Privacies in Networked Systems With Tokenized Polytopic Transforms

Abstract

This brief studies the problem of preserving state and control privacies in a networked system where the plant is a constrained discrete-time linear system with bounded disturbances. Our approach to this problem is a privacy-preserving control scheme which assigns integer tokens to the combination of affine transforms and qualifying polytopes within state constraints. Specifically, we utilize the piecewise affine property of a feedback control law obtained with the explicit model predictive control technique and design tokenized affine transforms for the control law and the polytopic partition of its domain. The qualifying polytopes are referred to as subregions and virtual subregions which are generated within the partitioned domain mentioned above. We show that the proposed scheme is semantically secure against privacy extraction by an eavesdropper. Simulation results demonstrate the validity of the proposed scheme in preserving state and control privacies.