MedRec-Secure: A Framework for Confidentiality Preservation in Medical Patient Records
Bibliographic record
Abstract
The digitization of healthcare has revolutionized patient data management through Medical Patient Records (MPRs), but has simultaneously introduced critical security vulnerabilities, as traditional cryptographic methods explicitly reveal the presence of sensitive information.This study introduces MedRec-Secure, a comprehensive data hiding scheme designed to enhance MPR confidentiality through a novel Dynamic Subtractor Selection Steganographic Framework (DSSSF) that conceals sensitive medical information within medical images while preserving diagnostic quality.The proposed framework operates on grayscale medical images divided into 4-pixel blocks, employing statistical analysis to select optimal reference pixels dynamically.A multi-zone embedding strategy categorizes pixels into three intensity zones, with tailored embedding rules for each zone.Experimental evaluation demonstrated superior performance, with a Peak Signal-to-Noise Ratio (PSNR) achieving a maximum of 75.43 dB across varying MPR payload sizes (1 kb to 100 kb).The Structural Similarity Index Measure (SSIM) achieved outstanding maximum values of 1.000, maintaining near-perfect similarity despite slight decreases with larger payloads.MedRec-Secure outperforms existing methods by 3.2 dB in PSNR performance, preserving diagnostic image integrity while enabling secure MPR transmission across healthcare networks.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".