A novel cryptographic framework and mathematical modeling for secure transmission of Parkinson’s disease data using RSA and block-based secret sharing
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".