Research on Privacy-preserving Monitoring in Edge Computing focuses on designing mechanisms that enable continuous observation, analytics, and control of edge devices and applications while safeguarding sensitive data and user privacy. This area addresses challenges such as distributed and heterogeneous edge infrastructures, dynamic workloads, constrained resources, and compliance with data protection regulations. Key research directions include privacy-aware data collection and aggregation, secure real-time monitoring frameworks, and anonymization techniques for edge-generated data. Other emerging topics involve federated and decentralized learning for privacy-preserving analytics, homomorphic encryption and differential privacy for secure monitoring, and lightweight monitoring protocols suitable for resource-constrained edge nodes. Additionally, research on adaptive, context-aware privacy policies, secure event logging, and AI-driven anomaly detection while maintaining privacy represents significant avenues for advancing trustworthy, efficient, and privacy-compliant edge computing systems.