Securing Voice Software Applications Using 5G, WSN and AI Driven Privacy Preservation Protocols
Hayder M. A. Ghanimi, K. Swaroopa, Amit Kr. Mishra, Anusha Papasani, Kolluru Suresh Babu, VijayaRangan Vivekanandhan · Journal of Machine and Computing · 2025
The reality-based, dynamic, and context-aware user experiences provided by voice software applications have contributed to their common acceptance. But, problems with data privacy and computer performance are challenges. In order to process voice data reliably, the present research proposes a secure integrated model of 5G-Wireless Sensor Networks with Artificial Intelligence (5G + WSN + AI) to apply privacy preservation protocols. To train decentralized models, the model used Federated Learning (FL). To prevent unauthorized inference, it deployed Secure Multi-Party Computation (SMPC). In the end, to secure sensitive data, it applied adaptive encryption methods. Word Error Rate (WER), Feature Extraction Accuracy (FEA), End-to-End Delay (EED), Network Throughput (NT), Packet Loss Rate (PLR), and Encryption Overhead (EO) represent several of the key performance measures that the model is considered superior to conventional networks such as SVPS, BDPS, GACS, and cloud-based centralized models. Additionally, it proved that next-generation Voice Learning Systems (VLS) are reliable, leveraging AI + 5G setup and maintaining robustness against privacy breaches in real-world asymmetric scenarios.