A Privacy-Aware and Traceable Fine-Grained Data Delivery System in Cloud-Assisted Healthcare IIoT
Bibliographic record
Abstract
The emerging of healthcare Industrial Internet of Things (HealthIIoT) cannot only facilitate high-quality care services for patients but also enable efficient telemedicine platform for healthcare practitioners. However, it faces several fundamental security and privacy challenges, such as secure fine-grained data delivery, privacy preserving keyword-based ciphertext retrieval, malicious key delegation, and efficiency of the system. To combat these issues, we propose a privacy-aware and traceable fine-grained system (PTFS) for secure data delivery in cloud-assisted HealthIIoT. Compared to the existing solutions that only implement some of the preceding features, the proposed solution enables secure fine-grained data delivery, privacy-preserving data retrieval, efficient encryption and decryption operations, and trace of malicious key delegation simultaneously. For security analysis, rigorous proofs of the proposed scheme are provided to prove its security. In addition, extensive simulations and experiments are conducted for performance evaluation, which demonstrate the feasibility and effectiveness of PTFS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".