Towards privacy-preserving cooperative control via encrypted distributed optimization
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Philipp Binfet
Philipp Binfet received a B.Sc. and an M.Sc. in Electrical Engineering from Paderborn University in 2019 and 2021, respectively. He is currently pursuing a Ph.D. in Control and Cyberphysical Systems Group at TU Dortmund University. His main research focus lies on distributed encrypted control schemes for multi-agent systems.
Janis Adamek received a B.Sc. and an M.Sc. in Electrical Engineering from the TU Dortmund University (TUD) in 2019 and 2022, respectively. He is currently pursuing a Ph.D. in Control and Cyberphysical Systems Group at TUD. His main research focus lies on the development of privacy-enhanced algorithms for control and machine learning.
Nils Schlüter received an M.Sc. in Mechatronics (with distinction) from the Leibniz University, Hanover, Germany in 2019. Currently, he is pursuing a Ph.D. in the Control and Cyberphysical Systems Group at TU Dortmund University. In his research, he focuses on the development of privacy-preserving control schemes for cloud-based services.
Prof. Dr.-Ing. Moritz Schulze Darup received a Diploma degree in Mechanical Engineering, a B.Sc. in Physics, and a Ph.D. in Control Engineering from the Ruhr-Universität Bochum (RUB) in 2008, 2010, and 2014, respectively. He became an Assistant Professor and leader of an Emmy Noether group for encrypted control in 2019 at Paderborn University. Since 2020, he has been a Full Professor for Control and Cyberphyiscal Systems at TU Dortmund University. His research interests include secure, predictive, and data-driven control.
Abstract
Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data are sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we focus on a secure distributed solution of a general consensus problem, which has manifold applications in cooperative control, by means of the alternating direction method of multipliers (ADMM). As a unique feature of our approach, we explicitly take into account the common situation that local decision variables contain copies of quantities associated with neighboring agents and ensure the neighbor’s privacy. We show the effectiveness of our method based on a numerical case study dealing with the formation of mobile robots.
Zusammenfassung
Kooperative Regelungen sind entscheidend für den effektiven Betrieb von dynamischen Multiagentensystemen. Insbesondere für verteilte Regelungen ist es essentiell, Daten zwischen den Agenten auszutauschen. Dies stellt ein Risiko für die Privatsphäre dar, falls die Daten sensibel sind. Verschlüsselte Regelung haben das Potenzial, dieses Risiko abzufangen und Vertraulichkeit zu gewährleisten. Die bestehenden Ansätze konzentrieren sich jedoch hauptsächlich auf Cloud-basierte Regelungen und verteilte Realisierungen sind restriktiv. In diesem Artikel stellen wir eine neuartige kooperative Regelung vor, die verschlüsselte verteilte Optimisierung nutzt, um die Privatsphäre der beteiligten Agenten zu gewährleisten. Genauer gesagt, konzentrieren wir uns auf eine sichere verteilte Lösung eines allgemeinen Konsensusproblems, das vielfältige Anwendungen in der kooperativen Regelung hat, mithilfe der ADMM (engl. für Alternating Direction Method of Multipliers). Ein Alleinstellungsmerkmal unseres Ansatzes ist, dass wir explizit die übliche Situation, dass lokale Entscheidungsvariablen Kopien von Variablen benachbarter Agenten enthalten, und gewährleisten hierfür die Privatsphäre der Nachbarn. Wir zeigen die Wirksamkeit unserer Methode anhand einer numerischen Fallstudie, die Formationen mobiler Roboter behandelt.
Funding source: German Research Foundation (DFG) and the Daimler and Benz Foundation
Award Identifier / Grant number: SCHU 2940/4-1, SCHU 2940/5-1, and32-08/19
About the authors
Philipp Binfet received a B.Sc. and an M.Sc. in Electrical Engineering from Paderborn University in 2019 and 2021, respectively. He is currently pursuing a Ph.D. in Control and Cyberphysical Systems Group at TU Dortmund University. His main research focus lies on distributed encrypted control schemes for multi-agent systems.
Janis Adamek received a B.Sc. and an M.Sc. in Electrical Engineering from the TU Dortmund University (TUD) in 2019 and 2022, respectively. He is currently pursuing a Ph.D. in Control and Cyberphysical Systems Group at TUD. His main research focus lies on the development of privacy-enhanced algorithms for control and machine learning.
Nils Schlüter received an M.Sc. in Mechatronics (with distinction) from the Leibniz University, Hanover, Germany in 2019. Currently, he is pursuing a Ph.D. in the Control and Cyberphysical Systems Group at TU Dortmund University. In his research, he focuses on the development of privacy-preserving control schemes for cloud-based services.
Prof. Dr.-Ing. Moritz Schulze Darup received a Diploma degree in Mechanical Engineering, a B.Sc. in Physics, and a Ph.D. in Control Engineering from the Ruhr-Universität Bochum (RUB) in 2008, 2010, and 2014, respectively. He became an Assistant Professor and leader of an Emmy Noether group for encrypted control in 2019 at Paderborn University. Since 2020, he has been a Full Professor for Control and Cyberphyiscal Systems at TU Dortmund University. His research interests include secure, predictive, and data-driven control.
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Research funding: Financial support by the German Research Foundation (DFG) and the Daimler and Benz Foundation under the grants SCHU 2940/4-1, SCHU 2940/5-1, and 32-08/19 is gratefully acknowledged.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Cybersecurity for industrial automation and control systems
- Forum
- Device discovery and identification in industrial networks
- Methods
- Towards privacy-preserving cooperative control via encrypted distributed optimization
- Secure bootstrapping for next-gen industrial automation systems
- Nachvollziehbare Security by Design-Entscheidungen für Automatisierungssysteme mittels funktionsbasierter Diagramme und Security-Bibliotheken
- A new hybrid risk assessment process for cyber security design of smart grids using fuzzy analytic hierarchy processes
- Applications
- Transformation in substation automation: Cyber-Resilient Digital Substations (CyReDS) in power grids
- Tools
- Integration of Cyber Threat Intelligence into Security Onion and Malcolm for the use case of industrial networks
Articles in the same Issue
- Frontmatter
- Editorial
- Cybersecurity for industrial automation and control systems
- Forum
- Device discovery and identification in industrial networks
- Methods
- Towards privacy-preserving cooperative control via encrypted distributed optimization
- Secure bootstrapping for next-gen industrial automation systems
- Nachvollziehbare Security by Design-Entscheidungen für Automatisierungssysteme mittels funktionsbasierter Diagramme und Security-Bibliotheken
- A new hybrid risk assessment process for cyber security design of smart grids using fuzzy analytic hierarchy processes
- Applications
- Transformation in substation automation: Cyber-Resilient Digital Substations (CyReDS) in power grids
- Tools
- Integration of Cyber Threat Intelligence into Security Onion and Malcolm for the use case of industrial networks
