A novel cryptographic framework and mathematical modeling for secure transmission of Parkinson’s disease data using RSA and block-based secret sharing
Why this work is in the frame
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Bibliographic record
Abstract
The secure transmission of medical data is an essential requirement in modern telemedicine systems, particularly for chronic neurological disorders such as Parkinson's disease. This paper proposes a novel hybrid cryptographic framework that combines RSA encryption with block-based secret sharing enhanced by a Hilbert matrix-driven mathematical model. The framework introduces dynamic block-wise key generation and adaptive sharing to strengthen data confidentiality and robustness against cryptanalytic attacks. Mathematical modeling is employed to analyze encryption stability, numerical conditioning of the Hilbert matrix, and the diffusion properties of the key space. The proposed method is validated using publicly available Parkinson's EEG and spiral drawing datasets, with quantitative analysis including encryption/decryption time, computational overhead, and image quality metrics (PSNR, SSIM). The framework is further benchmarked against AES-Shamir and ECC-based hybrid models. Experimental results indicate that the proposed system achieves higher security entropy and lower computational cost, making it suitable for deployment in resource-constrained medical IoT environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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