PPsky: Privacy-Preserving Skyline Queries with Secret Sharing in eHealthcare
Bibliographic record
Abstract
Applying skyline queries to medical data can considerably benefit medical analysis in eHealthcare. However, as medical data often involves sensitive personal data, privacy concerns have become a significant impediment to the development of eHealthcare. Although several privacy-preserving skyline query schemes in eHealthcare have been put forth, they need a trusted platform to generate and assign secret keys for the multi-source scenario. In addition, those schemes incur non-trivial computational costs on resource-limited entities. To address these limitations, we propose a novel privacy-preserving skyline query scheme, named PPsky, based on arithmetic secret sharing, in which a series of secure protocols are designed to handle the basic operations in skyline queries. Security analysis illustrates that our PPsky scheme is privacy-preserving, and the evaluation results also validate the efficiency of our PPsky scheme.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".