Smart Contract-Based Access Control Scheme for Blockchain Assisted 6G-Enabled IoT-Based Big Data Driven Healthcare Cyber Physical Systems
Bibliographic record
Abstract
6G (sixth-generation wireless), the successor to 5G cellular technology, operates at higher frequencies than its predecessor and supports significantly greater capacity and markedly reduced latency. Healthcare is treated as a complex system with various stakeholders, like doctors, patients, hospitals, pharmaceutical companies as well as healthcare decision-makers. The innovations in the Internet of Things (IoT) and incorporating emerging technology in the healthcare systems provide the quality of services to the people and save millions of lives. However, patient privacy and secure interchange of medical data from various healthcare providers need to be adequately addressed. Furthermore, incorporating blockchain in the healthcare system helps to make the system more transparent and secure due to inherent properties of the blockchain. In addition, Big Data analytics helps in analyzing large datasets from hundreds of patients, and then in identifying various clusters and correlation among datasets, and also in developing predictive models. In this paper, we aim to propose a new smart contract-based access control for 6G-enabled blockchain assisted in the healthcare system (in short, we call it as SACS). SACS provides a patient to communicate with its healthcare management authority securely and helps to interchange his/her medical information across healthcare providers. A detailed security analysis, experimental results and comparative study assure that the proposed SACS is secure by preventing possible active and passive attacks, and requires less computational and communication costs as compared to those for other relevant competing schemes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".