Efficient Data Integrity Verification Scheme Based on Multi-Branch Authentication Tree for Electronic Health Record
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integrity of electronic health record (EHR) is susceptible to compromise by hardware failures, software errors, or human errors. To date, numerous data integrity verification schemes have been proposed, but most face challenges related to third-party auditing and communication overhead. To address this, a novel EHR integrity verification scheme based on a multi-branch authentication tree is presented in this paper. By integrating an edge-based batch processing mechanism with data identity labeling technology, a low-overhead data verification framework is constructed, effectively reducing communication load. A minimal multi-branch tree structure is innovatively designed to enable parallel authentication and batch signing of data blocks. Concurrently, a random security code generation algorithm is introduced to ensure data security. Experimental and analytical results demonstrate that the proposed scheme maintains correctness, efficiency, and security, consistently achieving 100 % precision in detecting corrupted EHR data replicas. This scheme provides an efficient and reliable data integrity guarantee mechanism for EHR within edge computing environments and contributes significantly to building a trustworthy medical service system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it