Efficient and Privacy-Preserving Dynamic Spatial Query Scheme for Ride-Hailing Services
Bibliographic record
Abstract
With the prosperity of mobile internet and the pervasiveness of location-aware mobile terminals, online ride-hailing, a high-level location-based service (LBS) which relies on dynamic spatial query, has made our life more convenient. However, the flourish of ride-hailing service still faces many severe challenges since users' location privacy and service provider's data security. In this paper, we present an efficient and privacy-preserving dynamic spatial query scheme (TRACE) for ride-hailing service. With TRACE, users (i.e., consumers and vehicles) can access ride-hailing service without divulging their sensitive location information, meanwhile, the ride-hailing server can achieve the necessary commercial operating information while keeping its sensitive data (i.e., the space division information) confidential. Specifically, with two proposed efficient and secure spatial query algorithms, named FSSQ and ESVQ, all location-related data are encrypted by its owner before being sent out, and are calculated without decryption during the spatial query process. Therefore, consumers, vehicles, and service provider cannot obtain each other's sensitive information. Detailed security analysis shows that TRACE can resist various known security threats. Furthermore, TRACE is implemented in the real environment, and extensive simulation results over smart phones demonstrate that the scheme is highly efficient and can be implemented effectively.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".