Enhancing Security and Privacy in 5G Device-to Device Communication: A Secure Gale-Shapley Algorithm Approach

Device-to-Device (D2D) communication plays a crucial role in enhancing 5G network performance by improving spectral efficiency, reducing latency, and supporting applications such as the Internet of Things (IoT). However, the direct communication pathways in D2D introduce significant security and privacy risks, including unauthorized access, data interception, and privacy breaches. To mitigate these challenges, we propose a robust and adaptive security framework that integrates AI-enhanced physical layer key generation, full-duplex adaptive jamming, and differential privacy mechanisms. Our approach leverages secure multi-party computation (MPC) and lightweight encryption to protect user preferences and communication data during resource allocation while ensuring optimal system performance. AI-driven key generation dynamically adjusts to evolving network conditions, while full-duplex adaptive jamming effectively mitigates eavesdropping threats. We validate the proposed framework through extensive MATLAB simulations, demonstrating its ability to maintain high throughput and low latency even under diverse security threats. The results confirm the framework’s effectiveness in securing D2D communications, making it highly suitable for mission-critical 5G applications where both performance and security are essential. 

INDEX TERMS: 5G networks, device-to-device communication (D2D), security framework, adaptive jamming, AI-driven threat detection, physical layer security, differential privacy, multi-party computation (MPC), lightweight encryption, and resource allocation.