Privacy-Preserving Nonlinear Cloud-based Model Predictive Control via Affine Masking

TL;DR

This paper introduces an affine masking method for privacy-preserving nonlinear model predictive control in cloud environments, ensuring data privacy without compromising control performance.

Contribution

It proposes a simple affine transformation-based privacy scheme that masks plant data in cloud-based MPC, maintaining control efficacy and privacy against eavesdroppers.

Findings

Privacy is preserved without affecting MPC performance.

The method effectively masks states and control inputs from eavesdroppers.

Extended to output-feedback MPC with successful privacy guarantees.

Abstract

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.