Observability Blocking in a Linear Synchronization Network with Partial State Measurements

Abstract: Large-scale networked systems are increasingly vulnerable to adversaries that can infer system dynamics from a small set of compromised nodes. This paper addresses the problem of blocking such inference using limited state information. While existing state-feedback methods achieve observability blocking with eigenvalue preservation, they require full state access and are impractical for large networks. We propose multiple control strategies that operate under partial state measurements. The first approach employs output feedback to achieve observability blocking while preserving a subset of open-loop eigenvalues. The second approach leverages an observer to reconstruct the system state and enables full-state feedback control, preserving all eigenvalues and providing greater flexibility in sensor placement. We further extend the observer-based design to a distributed framework. Numerical examples demonstrate the scope and validity of the proposed methods.