SOREL: Efficient and Secure ORE-based Range Query over Outsourced 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
Outsourcing data to the cloud has become popular due to the big data challenges. However, security concerns compel the outsourced data to be encrypted before sending them to the cloud, which lowers their utility and efficiency. The range query plays a significant role in common queries. Consequently, how to efficiently support the range query over encrypted data has become an important challenge. Previously reported schemes either achieve efficiency only in a specific operation or have severe defects in scalability. To address these limitations, we propose a framework, called SOREL, which simultaneously considers security, efficiency and scalability. Specifically, we first propose a new efficient and Secure Order Revealing Encryption (SORE) scheme, which is more secure than bit-based ORE schemes. Then, by employing the proposed SORE scheme, we design a novel index within our framework SOREL to support efficient updating and query operations over encrypted data. Detailed security analysis shows that our SOREL achieves the desirable security requirements. Additionally, results from extensive evaluations indicate that i) SORE outperforms other alternative schemes by at least 8; and ii) SOREL is at least 3faster than the comparative schemes with range query operation in the best case while ensuring the competitiveness with insertion operation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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