Privacy in Distributed Control and Optimization
Notice bibliographique
Résumé
Distributed control and optimization form the backbone of many critical control systems, with typical examples including sensor networks, power grids, and intelligent transportation systems. As these systems grow in scale and complexity, ensuring the privacy of individual agents becomes increasingly essential. Privacy protection is no longer an optional add-on; it is a crucial element in the design of these systems. Sensitive data, such as states, measurements, and decisions, must be shielded from unauthorized access or malicious attacks, while still allowing the system to function effectively. This special issue aims to explore novel techniques and strategies that protect privacy in distributed control and optimization, showcasing how these approaches can be seamlessly integrated into real-world applications. The diverse contributions in this issue demonstrate the growing importance of privacy in the evolving field of distributed control systems. RNC7747 introduces a privacy-preserving method for coverage control in multi-agent systems with limited communication ranges, ensuring efficient operation without compromising agents' privacy. RNC7798 proposes a privacy-preserving consensus mechanism based on pulse-coupled oscillators, helping maintain privacy while agents coordinate in distributed networks. RNC7778 presents privacy preservation techniques for cloud-based cooperative LQG control systems, enabling privacy protection even when leveraging cloud resources. RNC8051 discusses achieving privacy-preserving average consensus in unreliable network environments, ensuring privacy despite communication challenges. RNC7929 introduces a novel approach to open privacy-preserving consensus via state decomposition, allowing agents to reach consensus while maintaining privacy. RNC7966 investigates leader-follower consensus in multi-agent systems with distributed event-triggered observations, ensuring privacy during agent coordination in real-time systems. RNC7789 discusses the tradeoff between privacy protection strength and control performance in control systems. RNC8029 proposes a new privacy mechanism for distributed average consensus on time-varying directed graphs. RNC7776 investigates event-triggered distributed optimization with differential privacy, reducing communication costs while safeguarding sensitive data. RNC7885 develops an event-triggered privacy-preserving optimization method for directed communication networks, emphasizing privacy during optimization tasks. RNC7791 focuses on robust optimization for virtual power plant scheduling under uncertainty, incorporating privacy preservation into the optimization process. RNC7730 explores prescribed-time distributed optimization with set constraints, offering time guarantees while maintaining privacy in dynamic optimization scenarios. RNC7926 provides a privacy-preserving algorithm for constrained resource allocation with communication delays, crucial for real-time decision-making in distributed systems. RNC7848 presents an adaptive event-triggered control strategy for cyber-physical systems under DoS and deception attacks, ensuring privacy and system security. RNC8068 addresses initial state privacy in nonlinear systems on Riemannian manifolds, offering methods to protect privacy in complex, high-dimensional control systems. RNC7906 focuses on dynamic consensus-based formation control for multi-robot systems, preserving privacy through output masking techniques while achieving coordination. RNC7688 introduces a periodic dynamic encoding mechanism for stealthy attack detection in distributed state estimation, focusing on security and privacy under attack scenarios. RNC70033 tackles robust consensus Kalman filtering for distributed state-saturated systems, incorporating dynamic-disturbed saturation levels and censored measurements to ensure privacy under uncertain conditions. The contributions in this special issue span a broad spectrum of topics related to privacy in distributed control and optimization. As distributed systems continue to evolve, these papers contribute to the ongoing conversation about balancing privacy protection with efficient system operation, highlighting the importance of privacy as a key consideration in the design and optimization of future distributed control systems. The authors declare no conflicts of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».