Verifiable and Privacy-Preserving Traffic Flow Statistics for Advanced Traffic Management Systems
Bibliographic record
Abstract
Crowdsourcing-based traffic monitoring plays an important role in advanced traffic management systems due to its high accuracy and low costs, but it may expose drivers real identities and sensitive locations that results in the privacy leakage of drivers. In this paper, we propose a crowdsourcing-based traffic monitoring scheme that enables a transportation management center (TMC) to achieve traffic flow statistics at road intersections in an efficient, verifiable, and privacy-preserving manner. Specifically, by integrating a homomorphic encryption primitive and a super-increasing sequence, traffic flow can be flexibly structured and encrypted by drivers, i.e., each drivers travel direction at T-junctions or crossroads is protected. As a middle-ware between drivers and TMC, roadside units (RSUs) are introduced to aggregate and further perturb the aggregated encrypted traffic flow based on a differential privacy mechanism. In this way, TMC is capable of acquiring the traffic flow statistics by decrypting the perturbed encrypted traffic flow, without disclosing each individual drivers traffic information. In addition, based on a lightweight commitment proof, the correctness of the encrypted drivers data can be guaranteed, i.e., a selfish driver cannot arbitrarily manipulate his data to poison the aggregated traffic flow. Finally, security analysis demonstrates that the proposed scheme satisfies all desirable security properties, including confidentiality, verifiability, unlinkability, and traceability. Extensive simulations are also conducted to show that the proposed scheme is efficient in terms of low computation and communication costs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".