Research on Pattern Recognition for Privacy in Edge Computing focuses on leveraging machine learning and pattern recognition techniques to protect sensitive information while enabling intelligent data processing at the edge. This area addresses challenges such as distributed and heterogeneous edge devices, dynamic workloads, limited computational resources, and the need to balance privacy preservation with real-time analytics. Key research directions include identifying and masking sensitive patterns in data streams, anomaly detection for privacy breaches, and context-aware data obfuscation techniques. Other emerging topics involve federated and decentralized learning for privacy-preserving pattern recognition, secure feature extraction, homomorphic encryption for pattern analysis, and AI-driven access control based on behavioral patterns. Additionally, research on adaptive, lightweight, and real-time privacy-preserving pattern recognition frameworks for IoT and edge applications represents significant avenues for advancing secure, intelligent, and privacy-compliant edge computing systems.