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