Modern autonomous systems—like self-driving cars, smart energy grids, and robotic teams—depend on massive amounts of data and complex algorithms to operate safely and efficiently. While this reliance enables powerful capabilities, it also opens the door to new vulnerabilities. In particular, these systems often share information across networks to coordinate actions, making them targets for cyber threats. Attackers can exploit shared data to uncover private details like vehicle locations or mission objectives. As these technologies become more embedded in everyday life, ensuring the privacy, security, and resilience of cyber-physical systems (CPS) is critical for public safety, especially in sectors like transportation, energy, and emergency response. This project tackles the dual challenge of privacy preservation and attack resilience in dynamic, networked CPS environments. It introduces a new framework that combines Guaranteed Privacy—a deterministic, performance-aware alternative to differential privacy—with robust control strategies grounded in set-valued estimation and resilient feedback design. The goal is to enable distributed CPS agents to operate safely, even when communication is intercepted or compromised. Beyond technical contributions, the project supports STEM education and workforce development through the Tulsa Cyber Camp, student research at the Center for Information Security (TUCIS), and partnerships with local industries and national labs. This awar