  We consider a cloud-based control architecture for a linear plant with
Gaussian process noise, where the state of the plant contains a client's
sensitive information. We assume that the cloud tries to estimate the state
while executing a designated control algorithm. The mutual information between
the client's actual state and the cloud's estimate is adopted as a measure of
privacy loss. We discuss the necessity of uplink and downlink privacy masks.
After observing that privacy is not necessarily a monotone function of the
noise levels of privacy masks, we discuss the joint design procedure for uplink
and downlink privacy masks. Finally, the trade-off between privacy and control
performance is explored.
