The expansion of cyber-physical systems (CPS), which integrate computational elements with physical processes, has revolutionized industries such as healthcare, transportation, energy, and manufacturing. However, CPS's increasing complexity and interconnectedness have exposed them to significant privacy and security threats. 

This Methods Collection focuses on leveraging the potential of generative AI to enhance the privacy and security of CPS. We invite original research, comprehensive reviews, and case studies that explore how generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, can be harnessed to detect, prevent, and mitigate threats while maintaining system integrity and user trust. 

Key topics of interest include: 

• Generative AI for Threat Detection and Mitigation: 
• AI-driven methods for anomaly detection in CPS 
• Generative models for simulating cyberattacks and designing robust defenses 
• Synthetic data generation for detecting malicious entities 
• Privacy Preservation in CPS Using Generative AI: 
• Privacy-preserving data sharing and federated learning with generative models 
• Synthetic data generation for secure and private CPS testing 
• Differential privacy techniques enhanced by generative AI 
• Adversarial Techniques and Countermeasures: 
• Simulation of adversarial attacks and their defenses in CPS using GANs 
• Strengthening generative AI models against adversarial attacks 
• Enhancing CPS resilience through adversarial training 
• Generative AI for Secure System Design: 
• Development of secure CPS architectures using AI-generated simulations 
• Risk assessment and prediction with generative AI 
• Ensuring model interpretability and explainability in secure AI applications 
• Applications and Case Studies: 
• Applications of generative AI in critical CPS domains, including smart grids, autonomous vehicles, and industrial IoT 
• Real-world case studies highlighting successful deployments of generative AI for CPS security 
• Ethical and regulatory considerations in the application of generative AI for CPS 

By consolidating cutting-edge techniques, practical applications, and interdisciplinary insights, this collection seeks to bridge the gap between research and practice. We encourage submissions that combine expertise in AI, cybersecurity, and CPS to address the pressing challenges faced by modern infrastructures.