Smart Encryption: A Novel Hybrid Approach to Selectively Encrypt Medical Images
Bibliographic record
Abstract
Selective encryption has emerged as a promising solution for securing medical images while maintaining computational efficiency; however, existing approaches often fall short in robustness and time-sensitive applicability. To address these gaps, this study proposes a hybrid selective encryption framework that combines Advanced Encryption Standard (AES) for regions of interest (ROI) with ChaCha20 for non-ROI regions in three-dimensional NIfTI cardiac MRI volumes from the ACDC dataset. Leveraging ground-truth segmentation masks, the framework targets diagnostically sensitive structures for robust cryptographic protection while accelerating the encryption of non-critical areas. Experimental results, validated by a new theoretical model, reveal a 60–70 percent reduction in encryption time compared to full-volume AES encryption. Security is validated using standard image encryption metrics, including entropy, correlation coefficient, Number of Pixels Change Rate, Unified Average Changing Intensity, Peak Signal-to-Noise Ratio, and Mean Absolute Error, all confirming its resilience to statistical and differential attacks. This approach achieves a practical balance between security and performance, offering adaptability for future integration into clinical systems such as DICOM-based teleradiology and cloud-driven medical imaging workflows.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".