VRepChain: A Decentralized and Privacy-Preserving Reputation System for Social Internet of Vehicles Based on Blockchain
Bibliographic record
Abstract
In the context of the social Internet of vehicles (SIoV), constructing reliable social relationships between dynamic and distributed entities is a challenging research problem. Rating-based reputation systems have been widely applied to assist human users in evaluating the honesty of target entities. However, the ratings in SIoV expose user privacy, including behavior, location, etc., which are required to be protected properly. Meanwhile, the blockchain technology with its distributed paradigm is potentially employed to protect information privacy. In this study, we propose the design of a blockchain-enabled reputation system named “VRepChain” for SIoV by especially considering the rating privacy issue. In our design, the ratings' privacy is strongly preserved in the processes of transmission and storage. The reputation of a vehicle is constructed based on the ratings with the agreement of the rating providers, ensuring the ratings are never abused by any other unauthorized entities during the usage process. Through experiments, the proposed system is demonstrated to improve the effectiveness of vehicles in terms of arriving at their destinations in a faster speed. Furthermore, the effectiveness of the constructed reputation model with untruthful ratings is extensively examined, showing its robustness and practicality in realistic applications.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".