EPSim-GS: Efficient and Privacy-Preserving Similarity Range Query over Genomic Sequences
Bibliographic record
Abstract
Similarity query over genomic sequences has played a significant role in personalized medicine and has applications in various fields, including DNA alignment and genomic sequencing. Since handling genomic sequences requires massive storage and considerable computational capacity, service providers prefer to process similarity queries over genomic sequences on cloud servers rather than at the client side. Due to the sensitivity of genomic sequences, preserving the privacy of queries has attracted considerable attention, and as a result, genomic sequences are demanded to be outsourced in an encrypted form. Although many schemes have been proposed for similarity queries over encrypted genomic data, they are either inefficient or have limitations in supporting the dynamic update of the dataset. To address the challenges, we propose an efficient and privacy-preserving similarity range query scheme, namely EPSim-GS. First, we introduce how to build a hash table to index the dataset, and present a similarity range query algorithm based on the hash table. Then, we design two cloud-based privacy-preserving protocols based on the Paillier cryptosystem to support the similarity range query algorithm over the encrypted dataset. After that, we propose EPSim-GS by leveraging the two privacy-preserving protocols. We then analyze the security of EPSim-GS and prove that it is privacy-preserving. Finally, we perform experiments to evaluate the scheme’s performance, and the results indicate that it is computationally efficient.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".