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.

翻译：我们针对网络化控制系统（NCSs）中最大化无限时域隐私的扭曲机制合成问题展开研究。考虑随机线性时不变系统，其状态信息通过含噪声的传感器测量获取，并通过非安全/公共通信网络传输至（可能具有对抗性的）远程站以计算控制动作（远程LQR控制器）。由于网络/站点的不可信性，攻击者可能访问传感器与控制数据并估计系统状态。为缓解此风险，我们在传输前对传感器与控制数据施加扭曲（隐私保护）机制，并将扭曲后的数据经过通信网络发送。该机制包含线性坐标变换与加性依赖高斯的随机向量。我们将扭曲机制的合成问题建模为凸规划。在此凸规划中，我们最小化系统状态与其在远程站最优估计之间的无限时域互信息（隐私度量），同时保证由扭曲机制引起的控制性能退化（LQR代价）不超过预设上界。