EFRS:Enabling Efficient and Fine-Grained Range Search on Encrypted Spatial Data
Bibliographic record
Abstract
Range search of spatial data, has been applied in many scenarios such as geometric queries, location-based services, and computational geometry, etc. Due to the increasing amount of spatial data, which are usually outsourced to the cloud for saving storage and computational overhead. However, a common privacy issue is that the cloud server may steal user's sensitive information utilizing its powerful computing advantages. A feasible way of managing this bottleneck is to encrypt spatial data before outsourcing it. Nevertheless, the availability of data will be significantly reduced because of the query difficulty over the encrypted cloud data. In this paper, we propose an Efficient Range Search scheme (EFRS) which can achieve fine- grained query over encrypted spatial data. We original contributions are threefold. First, polynomial fitting technique and orderpreserving encryption are introduced to realize the efficient and fine- grained range query over encrypted cloud data. Then, in order to improve the search efficiency, we exploit the Rtree to significantly decreased the search space. Finally, we theoretically proved the security of our proposed scheme in terms of confidentially of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoor. Besides, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".