Preserving Privacy in the Age of Wireless Evolution: Differential Privacy in 5G, Beyond 5G, and 6G

As wireless communication continues its rapid evolution into the context of 5G, Beyond 5G, and the anticipated 6G, the demand for seamless connectivity and data-driven services is met with growing concerns about individual privacy. The advent of 5G introduced unparalleled speed and connectivity, enabling an array of transformative applications. However, it also exposed vulnerabilities in user privacy. Beyond 5G and 6G promise even more ambitious goals, such as ultra-low latency and ubiquitous connectivity, intensifying the need for robust privacy safeguards. This work explores the vital role of Differential Privacy (DP) as a privacy-preserving mechanism in the context of these next-generation wireless networks. DP is a mathematical framework that ensures statistical queries on sensitive data do not compromise individual privacy. This research delves into the core principles of DP and its practical applications in the wireless domain, illustrating how it can protect user data while enabling data sharing for network optimization, security, and advanced services. It also provides some experimental results on two important aspects of wireless communication: network slicing and network traffic prediction. Finally, it highlights the applicability of DP and the delicate balance between connectivity and confidentiality and underscores the significance of privacy preservation in shaping the future of wireless technology.