The operation of emerging large-scale monitoring and control systems in CPSs relies on incessantly data collected from the sensors surrounding human beings. This may cause privacy leakage, since many sensitive information about age or health carried in the data may be revealed by malicious agents. For example, the smart meters communicate fine-granular electricity consumption data to the electricity provider. By employing the None-Intrusive Load Monitoring (NILM) technology, attackers can infer the daily schedule of the inhabitants from the electricity consumption, and easily break into the house when nobody is at home.
Regarding the remote state estimation, due to the broadcasting nature and the open medium of the wireless communication (between sensors and remote estimators), the nearby receivers can intercept the transmitted signals and estimate the crucial state information of a dynamic process. Hence, a tradeoff between the accuracy and privacy of the estimation performance is established. With the consideration of dynamic physical processes and time-varying data streams in CPSs, the main research interest is investigating an energy-efficient scheme to control the disclosure of state information to the eavesdroppers and meanwhile guarantee the remote estimation accuracy, especially through an information-theoretical approach.