  With the advent of 5G technology that presents enhanced communication
reliability and ultra low latency, there is renewed interest in employing cloud
computing to perform high performance but computationally expensive control
schemes like nonlinear model predictive control (MPC). Such a cloud-based
control scheme, however, requires data sharing between the plant (agent) and
the cloud, which raises privacy concerns. This is because privacy-sensitive
information such as system states and control inputs has to be sent to/from the
cloud and thus can be leaked to attackers for various malicious activities. In
this paper, we develop a simple yet effective affine masking strategy for
privacy-preserving nonlinear MPC. Specifically, we consider external
eavesdroppers or honest-but-curious cloud servers that wiretap the
communication channel and intend to infer the plant's information including
state information and control inputs. An affine transformation-based
privacy-preservation mechanism is designed to mask the true states and control
signals while reformulating the original MPC problem into a different but
equivalent form. We show that the proposed privacy scheme does not affect the
MPC performance and it preserves the privacy of the plant such that the
eavesdropper is unable to identify the actual value or even estimate a rough
range of the private state and input signals. The proposed method is further
extended to achieve privacy preservation in cloud-based output-feedback MPC.
Simulations are performed to demonstrate the efficacy of the developed
approaches.
