Efficient Lightweight Cryptographic Framework for Securing Medical Images in IoT Systems
Bibliographic record
Abstract
In the modern era of cloud computing and the internet of things (IoT), the safe transfer of medical images is crucial.In healthcare systems, medical images play a crucial role in diagnoses.Images such as X-rays, ultrasounds, CT scans, MRIs, and brain scans of patients include private and sensitive information.Unfortunately, unauthorized people could view these photos illegally, using them for non-diagnostic purposes due to weak communication channel security and vulnerabilities in healthcare facilities' storage systems.One standard method for protecting sensitive medical image from attackers is image encryption, which also helps keep data transmission and storage systems secure.A hybrid stream cipher and pixel rearrangement for rows and columns form using parallel processing is the basis of our proposed lightweight cryptosystem.An integral part of medical image encryption is the key generation process, which uses a logistic map and a set of linear feedback shift register (LFSRs) to generate a stream of bytes at random.The suggested method is efficient based on metrics like peak-to-signal noise ratio, encryption time, information entropy, number of pixels changing rate, histogram analysis, and mean square error (MSE).Experiments have proven that the suggested cryptosystem is an effective means of encrypting sensitive patient data stored in images while remaining lightweight.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".