Preserving Privacy in Cloud-based Data-Driven Stabilization
Abstract
In the 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 systems.The main objective of this research is to preserve the privacy for 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 is applicable to both data with and without disturbance and is lightweight in terms of computational overhead. Numerical investigations for a case study demonstrate the impacts of our mechanism and its role in hindering malicious adversaries from achieving their goals.
Cite
@article{arxiv.2410.17353,
title = {Preserving Privacy in Cloud-based Data-Driven Stabilization},
author = {Teimour Hosseinalizadeh and Nima Monshizadeh},
journal= {arXiv preprint arXiv:2410.17353},
year = {2025}
}