Secure Similar Patients Query on Encrypted Genomic Data
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
Both individuals and enterprises produce genomic data rapidly and continuously. There is a need to outsource such data to the cloud for better flexibility. Outsourcing also helps data owners by eliminating the local storage management problem. To protect data privacy and security, data owners must encrypt the sensitive data before outsourcing. Since genomic data are enormous in volume, executing researchers queries securely, and efficiently is a challenging task. In this paper, we introduce an indexing algorithm based on the prefix-tree to support similar patient queries. The proposed method guarantees the following: data privacy, query privacy, and output privacy. The privacy is guaranteed through encryption and garbled circuits considering the semi-honest adversary model. The overall computation is scalable and fast enough for real-life biomedical applications. Moreover, experimental results show that our method performs better than existing state-of-art techniques in this domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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