Towards Privacy-Preserving Online Medical Monitoring with Reverse Skyline Query
Bibliographic record
Abstract
With the flourish of Wireless Body Area Network (WBAN), the online medical monitoring system has attracted extensive attention. Meanwhile, due to the limited resources, the hospital tends to outsource the medical services to the cloud and requires the patients' data to be encrypted before uploading. It is bound to raise a challenge in data availability, e.g., the reverse skyline query that is widely used in monitoring systems. In this paper, we propose a privacy-preserving online medical monitoring system, in which the cloud can answer the reverse skyline query over encrypted data and return the monitored data of high-risk patients to a doctor. To achieve this goal, we first design four secure protocols that can ensure the security of operands while minimizing the communication costs between two cloud servers. Based on these privacy-preserving protocols, we propose two privacy-preserving reverse skyline query schemes that can be used in the monitoring system. Security analysis shows that our proposed scheme is indeed privacy-preserving, and performance evaluations also demonstrate the efficiency of our scheme in terms of computation and communication.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".