Efficient and Privacy-Preserving Similar Patients Query Scheme Over Outsourced Genomic Data
Bibliographic record
Abstract
Over the past decade, genomic data has grown exponentially and is widely used in promising medical and health-related applications, which opens up new opportunities for the field of medicine. Similar patients query (SPQ), which can help physicians formulate an optimal therapy, is one of such popular applications. Despite its popularity, since human genomes are usually highly sensitive, a series of policies have been launched by the government to strictly control its acquisitions and utilization. Thus, how to prevent privacy disclosure becomes of great importance to the flourish of SPQ services. In this article, aiming at the above challenge, we first design a novel genetic BK-tree (GBK-tree) for a genomic database. Then, combined with a random sorting mechanism and some existing encryption techniques, we propose an efficient and privacy-preserving similar patients query scheme over encrypted cloud data, named CASPER. With CASPER, a medical institution can securely outsource its private genomic database to a cloud server, and physicians can request SPQ services from the cloud server while keeping her/his query secret. Detailed security analysis shows that CASPER can preserve privacy in the presence of different threats. Furthermore, extensive performance evaluations demonstrate the high accuracy and efficiency of our proposed 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".