Efficient privacy-preserving circular range search on outsourced spatial data
Bibliographic record
Abstract
With the growing popularity of outsourcing data and services to the cloud, performing queries on encrypted data becomes a promising technique. Searchable encryption (SE) allows encryption while still enabling search for a variety of data. However, most of the existing arts focus on rectangular range query on common database. Query on encrypted spatial database has not been well studied. Moreover, as a vital type of geometric query on spatial data, the circular range search (CRS) is widely utilized in Location-Based Services (LBSs) and computational geometry. A recently proposed CRS scheme achieved security and privacy requirements. However, it exhibits low performance in terms of encryption and search efficiency. In this paper, we propose an Efficient Privacy-preserving CRS scheme (EP-CRS) on outsourced spatial data. Specifically, our scheme achieves CRS by leveraging an R-tree based SE scheme and adding a trusted-third party (TTP) to system. Security analysis indicates that EP-CRS can preserve data and query privacy. In addition, we conduct real experiments and compare EP-CRS with the existing one to show that the proposal is more efficient in terms of data encryption, token generation and search.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".