Privacy in edge computing is a critical research area that addresses the protection of sensitive data in distributed, resource-constrained, and latency-sensitive environments. Research papers in this domain explore techniques to safeguard user and device data during collection, processing, storage, and transmission across edge, fog, and cloud layers. Studies highlight privacy-preserving frameworks such as federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and blockchain-based mechanisms tailored for edge devices with limited computation and energy resources. Recent works focus on balancing privacy protection with system performance, ensuring low-latency, real-time analytics while minimizing overhead. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and mobile edge computing, where sensitive information such as personal health data, location, and behavioral patterns must be securely processed. Additionally, research investigates adaptive privacy policies, context-aware data anonymization, and compliance with regulations such as GDPR to ensure trustworthy and accountable edge computing environments. Overall, privacy research in edge computing emphasizes designing intelligent, scalable, and secure frameworks that protect sensitive data without compromising performance or functionality.