An Efficient Ciphertext-Policy Weighted Attribute-Based Encryption for the Internet of Health Things
Bibliographic record
Abstract
The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".