Preserving Privacy in Cloud-Based Data-Driven Stabilization

In recent years, we have observed three significant trends in control systems: a renewed interest in data-driven control design, the abundance of Cloud computational services, and the importance of preserving privacy for the system under control. Motivated by these factors, this work investigates privacy-preserving outsourcing for the design of a stabilizing controller for unknown linear time-invariant (LTI) systems. The main objective of this research is to preserve the privacy of the system dynamics by designing an outsourcing mechanism. To achieve this goal, we propose a scheme that combines transformation-based techniques and robust data-driven control design methods. The scheme preserves the privacy of both the open-loop and closed-loop system matrices while stabilizing the system under control. The scheme applies to both data with and without disturbance and is lightweight in terms of computational overhead compared to cryptography-based methods. Numerical investigations for a case study demonstrate the impacts of our mechanism and its role in hindering malicious adversaries from achieving their goals.