A Novel AI-Driven Homomorphic Encryption Framework for Secure Real-Time Telehealth Data Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.078 | 0.064 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it