Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data
Bibliographic record
Abstract
Location-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.005 |
| 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".