An Efficient and Privacy-Preserving Route Matching Scheme for Carpooling Services
Bibliographic record
Abstract
With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and makes our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users’ location or route privacy. In this article, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser–Micali-based equality determination algorithm (GMEDA), we design an accurate similarity computation algorithm (ASCA), which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the reverse Minhash (RM) method is also designed to construct a dissimilar route filter algorithm (DRFA), which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Security analysis shows that TAROT can protect users’ location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly efficient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".