Improved Vigenere Cipher-RSA-Based Medical Image Security Through Multiple Encryption Keys
Bibliographic record
Abstract
With the rapid evolution of telecommunication technologies, new means to share patients' medical images have consistently developed, leading to changes in their protection strategies.Consequently, researchers are paying attention to increasing security and privacy of sensitive medical images.However, brute-force, geometric and non-geometric attacks and unlawful manipulation have occurred in recent years.This paper attempts to propose a robust and hybrid encryption approach using improved Vigenere cipher and RSA that helps enhance security and integrity in medical images and protect sensitive data.The medical images come from different modalities such as X-ray, CT and MRI.The traditional public and private keys for the RSA algorithm is enhanced by adding a second key to the medical image.The second key and RSA keys are used to encode and decode the image which makes the decryption process considerably more difficult with correct key combinations.By incorporating the second key, the proposed approach addresses the challenges related to confidentiality and security in medical image transmission.Therefore, the proposed approach shows promising results in enhancing security and providing good performance of image encryption/decryption processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".