"Encrypted AI in 5G Networks: Privacy and Performance Trade-offs"

Abstract

The integration of Artificial Intelligence (AI) within 5G networks promises enhanced performance and innovative applications. However, this integration raises significant privacy concerns, particularly regarding the handling and transmission of sensitive data. This paper explores the intersection of AI and 5G, focusing on the implementation of encrypted AI to address privacy issues while assessing the potential trade-offs in network performance. Encrypted AI techniques, including homomorphic encryption and federated learning, are evaluated for their effectiveness in securing data without compromising the benefits of AI-driven enhancements in 5G networks. Through comprehensive analysis and simulation, we highlight the balance between maintaining robust privacy safeguards and achieving optimal network efficiency. Our findings provide critical insights for network architects and policymakers aiming to develop secure and high-performing 5G infrastructures, ultimately contributing to the safe deployment of AI technologies in future communication systems.