Parallel and private generalized suffix tree construction and query on 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
BACKGROUND: Several technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery. METHODS: In this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework. RESULTS: The experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup.
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 | 0.003 | 0.019 |
| 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