Secure Data Sharing and Prediction with Digital Twin and Blockchain in Healthcare
Bibliographic record
Abstract
The rapid implementation of the fifth-generation wireless networks has driven advances in digital twin (DT) technique, which has been widely used, especially in healthcare. However, the accessibility of data raises concerns about privacy, security, and accountability among participants, affecting overall security and performance of the healthcare DT system. In this article, we investigate a blockchain- based secure healthcare digital twin data (HDTD) sharing framework to address data privacy concerns. In the blockchain-based secure HDTD sharing model, we propose the access control scheme through cloud storage and attribute encryption to realize the secure data interaction between different users. Based on this, we design an HDTD missing value prediction algorithm in order to solve the problem of missing valid data due to data tampering or loss with limited resources and to meet the real-time requirements of data interaction in DT. The experimental results show that compared with the existing schemes, the proposed blockchain- based secure HDTD sharing scheme has superior performance in improving data security and reducing data interaction delay. The article outlines key technical challenges and future directions for blockchain-based HDTD research.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".