A Novel AI-Driven Homomorphic Encryption Framework for Secure Real-Time Telehealth Data Analysis

Abstract

Ensuring privacy in AI-driven telehealth analytics remains a persistent challenge, as conventional cryptographic methods struggle to meet real-time and compliance requirements. This research developed and validated an AI-driven homomorphic encryption framework for secure real-time telehealth data analysis, addressing critical privacy challenges in medical IoT systems. The study designed a proactive threat intelligence system, developed a predictive analytics framework, and guided secure implementation. A review of existing cryptographic solutions identified gaps in scalability and real-time processing. Using a quantitative experimental design, synthetic telehealth datasets, hybrid CKKS-BFV schemes, and neural network optimization were employed. Implementation in Python with SEAL and TensorFlow was tested across computational, security, and compliance metrics. Results showed a 23.7% overhead reduction, sub-535 ms latency for 5,000 records/sec, and 96.9% HIPAA compliance, with attack success rates below 6%. Synthetic data achieved 99.3% quality, and performance improvements over AES-256 and Paillier were statistically significant (p < 0.001). The hybrid scheme outperformed single approaches by 18.4%, supporting scalable, accurate analytics. Despite synthetic data limitations, findings confirm the framework’s ability to secure telehealth data and enhance clinical decision-making. Future work includes real-world dataset development, explainable AI integration, clinical deployments, and adaptive algorithms for emerging threats.