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We address the problem of synthesizing distorting mechanisms that maximizeinfinite horizon privacy for Networked Control Systems (NCSs). We considerstochastic LTI systems where information about the system state is obtainedthrough noisy sensor measurements and transmitted to a (possibly adversarial)remote station via unsecured/public communication networks to compute controlactions (a remote LQR controller). Because the network/station isuntrustworthy, adversaries might access sensor and control data and estimatethe system state. To mitigate this risk, we pass sensor and control datathrough distorting (privacy-preserving) mechanisms before transmission and sendthe distorted data through the communication network. These mechanisms consistof a linear coordinate transformation and additive-dependent Gaussian vectors.We formulate the synthesis of the distorting mechanisms as a convex program. Inthis convex program, we minimize the infinite horizon mutual information (ourprivacy metric) between the system state and its optimal estimate at the remotestation for a desired upper bound on the control performance degradation (LQRcost) induced by the distortion mechanism.

Infinite Horizon Privacy in Networked Control Systems: Utility/Privacy Tradeoffs and Design Tools