vPass: Publicly Verifiable Fair Exchange Protocol for Vehicle Passports
Bibliographic record
Abstract
In second-hand vehicle markets, blockchains are being proposed as means to provide verification of vehicle history, a.k.a. vehicle passport (VP). However, given that confidentiality of VPs often contradicts public verification, blockchains are not used to their full potential in the proposed frameworks. Specifically, although blockchain smart contracts offer a decentralized mechanism for untrusted parties to fairly exchange digital assets without the need for a trusted third party, VP exchange is always carried off-chain. In this work, we investigate the problem of “fair exchange” of confidential VPs over public blockchains where its plain information must be verified against its publicly committed value. We propose a zero-knowledge proof, called Consistent Commitment Encryption (CCE), that enables the public verification of the consistency between ElGamal encryption of a given VP and its Pedersen commitment. We employ our CCE to build vPass, a decentralized vehicle passport framework that enables second-hand vehicle buyers to purchase vehicle history information from designated service providers and get it verified and delivered on-chain while preserving its confidentiality. The security of CCE relies on the intractability of the discrete logarithm problem in elliptic curve groups and it has no trusted setup. We formally prove that CCE is sound, complete, and witness indistinguishable proof of knowledge, and report on comparisons with other generic proof systems. Moreover, we show that vPass provides fair exchange and confidentiality of the vehicle history, and compare it to existing VP systems. Finally, we provide a proof of concept implementation on Ethereum and report the system performance metrics.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".