The exponential growth of social media platforms has created unprecedented challenges in safeguarding user privacy and ensuring secure data management. High-profile cases of data misuse, such as those involving Facebook and Meta, have highlighted the urgent need for stronger privacy-preserving mechanisms in large-scale digital ecosystems. As billions of users generate real-time data, centralized processing models struggle with issues of latency, security vulnerabilities, and data breaches. Edge computing offers a promising paradigm by processing data closer to the source, thereby reducing reliance on centralized servers and minimizing exposure to privacy risks. This chapter examines privacy-preserving strategies for edge networks, drawing insights from notable social media data protection cases. We explore how blockchain integration, federated learning, and AI-driven anomaly detection can strengthen privacy safeguards while maintaining system performance. Special emphasis is placed on designing privacy-aware architectures that balance low latency, scalability, and user trust. By linking lessons from social media controversies to technical innovations in edge computing, this chapter provides both theoretical insights and practical implications for researchers, policymakers, and industry practitioners. Ultimately, the study argues that privacy-preserving edge networks are essential for ensuring secure, transparent, and user-centric digital environments in the era of 5G, 6G, and beyond.