Preserving Privacy in Cloud-based Data-Driven Stabilization
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912336060612608 |
|---|---|
| author | Hosseinalizadeh, Teimour Monshizadeh, Nima |
| author_facet | Hosseinalizadeh, Teimour Monshizadeh, Nima |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preserving Privacy in Cloud-based Data-Driven Stabilization Hosseinalizadeh, Teimour Monshizadeh, Nima Systems and Control 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. |
| title | Preserving Privacy in Cloud-based Data-Driven Stabilization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.17353 |