Towards Private Similarity Query based Healthcare Monitoring over Digital Twin Cloud Platform
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
As the growing proportion of aging population, the demand for sustainable, high quality, and timely healthcare services has become increasingly pressing, especially since the outbreak of COVID-19 pandemic in the early of 2020. To meet this demand, a promising strategy is to introduce cloud computing and digital twin techniques into the healthcare systems, where the cloud server is employed for storing healthcare data and offering efficient query services, and the digital twin is used for building digital representation for patients and leverages the query services of the cloud server to monitor healthcare states of patients. Although several cloud computing and digital twin based healthcare monitoring frameworks have been proposed, none of them has considered the data privacy issue, yet the leakage of the private healthcare information may cause catastrophic losses to patients. Aiming at the challenge, in this paper, we propose an efficient and privacy-preserving similarity query based healthcare monitoring scheme over digital twin cloud platform, named PSim-DTH. Specifically, we first formalize a similarity query based healthcare monitoring model over digital twin cloud platform. Then, we deploy a partition-based tree (PB-tree) to index the healthcare data and introduce matrix encryption to propose a privacy-preserving PB-tree based similarity range query (PSRQ) algorithm. Based on PSRQ algorithm, we propose our PSim-DTH scheme. Both security analysis and performance evaluation are extensively conducted, and the results demonstrate that our proposed PSim-DTH scheme is really privacy-preserving and efficient.
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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 | 0.000 | 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 | 0.001 |
| 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 it