Efficient and Privacy-Preserving Dynamic Spatial Query Scheme for Ride-Hailing Services
Why this work is in the frame
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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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.013 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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