An Efficient Privacy-Preserving Authenticated Key Establishment Protocol for Health Monitoring in Industrial Cyber–Physical Systems
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
Industry 5.0 is the automation, digitization, and data communication of the industrial procedure that comprises industrial cyber–physical systems (I-CPSs), industrial Internet of Things (IIoT), and artificial intelligence (AI). In the I-CPS-enabled healthcare ecosystem, intelligent wearable devices have been extensively employed to sense body information and measure the health status of the patients. Besides other IIoT applications, the I-CPS-enabled healthcare ecosystem also bears various challenges. For instance, due to the communal communication mediums, the security of a patient’s physiological datum is becoming a significant challenge these days. In order to cope with this challenge, we presented a secure and lightweight key establishment protocol. To the best of our knowledge, this protocol is the first application of physically unclonable function (PUF) in the I-CPS-enabled healthcare. The security of the designed protocol is proved with the help of a widely recognized real-or-random (ROR) model. The practical demonstration of our protocol from the network perspective is also measured through broadly recognized NS3 simulator tool.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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