Research on Privacy in Edge Computing focuses on protecting sensitive data, user identities, and operational information while enabling computation and analytics at the network edge. This area addresses challenges such as heterogeneous and resource-constrained edge devices, distributed data sources, dynamic workloads, and compliance with privacy regulations. Key research directions include privacy-preserving data aggregation, anonymization, and encryption techniques, as well as secure data sharing and access control mechanisms tailored for edge infrastructures. Other emerging topics involve federated learning and decentralized AI for privacy-preserving analytics, blockchain-enabled privacy and trust management, and lightweight privacy-preserving protocols suitable for IoT and mobile edge devices. Additionally, research on context-aware privacy policies, adaptive privacy mechanisms for dynamic edge environments, and secure collaboration between edge and cloud systems represents significant avenues for advancing secure, user-centric, and privacy-compliant edge computing solutions.