Homomorphic Encryption-Based Testing for Secure Mobile Healthcare Data Processing in 5G-Enabled Systems
Keywords:
Homomorphic Encryption, Secure Multiparty Computation, 5G Cloud Computing, Privacy-Preserving Healthcare, AI-driven Analytics, Federated Learning, Real-Time Diagnostics.Abstract
The advancement of 5G-enabled mobile healthcare systems has improved real-time patient monitoring, diagnostics, and data security. However, ensuring privacy-preserving data processing remains a challenge. This study proposes a Homomorphic Encryption-Based Testing framework integrating Fully Homomorphic Encryption (FHE), Secure Multiparty Computation (MPC), and AI-driven analytics for secure mobile healthcare data processing. The proposed method incurs encryption (2.1 ms), decryption (2.6 ms), processing latency (4.7 ms), computational overhead (15.7%), and the data transmission efficiency is 48.3 Mbps, which considerably surpasses the traditional encryption methods. The framework supports privacy-preserving AI-driven diagnostics in real time over 5G networks encrypted data, therefore HIPAA and GDPR-compliant. Future work includes (1) optimizing computation efficiency, (2) exploring expanded federated learning securely, and (3) enhancing real-time encrypted AI-driven healthcare analytics.
