FLIP: An Efficient Privacy-Preserving Protocol for Finding Like-Minded Vehicles on the Road
Bibliographic record
Abstract
Vehicle chatting is one of the most promising applications in VANETs, which allows like-minded vehicles to chat on the topics of common interest on the road. However, there exist some newly emerging privacy challenging issues in vehicle chatting application, such as how to find a like-minded vehicle on the road and how to prevent one's interest privacy (IP) from others who are not like-minded? In this paper, to tackle these challenging issues, we propose an efficient privacy-preserving \underline{f}inding \underline{l}ike-minded veh\underline{i}cle \underline{p}rotocol (FLIP), and apply the provable security technique to demonstrate its security. In addition, extensive simulations are also conducted to examine its practical considerations, i.e., the relation between the expected IP-preserving level and the delay of finding like-minded vehicles on the road.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".