Security and Privacy of Patient Information in Medical Systems Based on Blockchain Technology
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 essence of “blockchain” is a shared database in which information stored is un-falsifiable, traceable, open, and transparent. Therefore, to improve the security of private information in medical systems, this article uses blockchain technology to design a method to protect private information in medical systems and effectively realize anti-theft control of private information. First, the Patient-oriented Privacy Preserving Access Control model is introduced into the access control process of private information in medical systems. Next, a private information storage platform is built by using blockchain technology, and information transmission is realized using standard cryptographic algorithms. In this process, file authorization contracts are also used to guarantee the security of private information and further prevent theft of medical private information. Our simulation results show that the storage response time of this method is kept below 1,000 ms, and the maximum information throughput rate reaches 550 kbit/s, which indicates that this method has strong performance in information storage and transmission efficiency. Moreover, the reliability and bandwidth utilization of data transmission across domains is higher, so the method has higher information security control performance and superior overall performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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