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

Abstract

Device-to-Device (D2D) communication is pivotal in enhancing the performance of 5G networks by improving spectral efficiency, reducing latency, and supporting applications such as the Internet of Things (IoT). Despite these benefits, direct communication pathways in D2D pose significant security and privacy challenges, such as unauthorized access, data eavesdropping, and privacy breaches. To address these issues, we propose a robust and adaptive security framework that integrates AI-enhanced physical layer key generation, full-duplex adaptive jamming, and differential privacy. Our approach employs 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 adapts to changing network conditions, whereas the full-duplex adaptive jamming mechanism effectively counteracts eavesdropping threats. We validated the framework through extensive MATLAB simulations, demonstrating its ability to achieve high throughput and low latency, even in the presence of various security threats. The results confirm the efficacy of the framework in safeguarding D2D communications, making it well suited for mission-critical 5G applications, where both performance and security are paramount.