Pattern recognition for privacy in edge computing is an emerging research area that focuses on using intelligent algorithms to identify sensitive information, detect anomalous behaviors, and enforce privacy-preserving mechanisms directly at edge devices. Research papers in this domain explore machine learning, deep learning, and hybrid pattern recognition techniques to analyze data streams from IoT devices, autonomous systems, smart healthcare applications, and industrial IoT while minimizing the exposure of private or sensitive information. Studies emphasize real-time, on-device processing to reduce reliance on centralized cloud systems, thereby enhancing data confidentiality and minimizing transmission of sensitive data. Recent works also integrate privacy-preserving approaches such as federated learning, differential privacy, and secure multiparty computation with pattern recognition models to maintain high accuracy while protecting user data. Additionally, context-aware and adaptive pattern recognition frameworks are investigated to dynamically adjust privacy levels based on user behavior, environment, and application requirements. Overall, pattern recognition for privacy in edge computing enables intelligent, real-time, and secure management of sensitive information while supporting scalable, low-latency, and trustworthy edge services.