Blockchain-Based Privacy-Preserving Authentication Model Intelligent Transportation Systems
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) have gained popularity due to smart services and applications to facilitate the users on the roads. The increasing growth of users in these networks created new and complex data processing, storage, security, and privacy concerns. These networks are using centralized edge, fog, or cloud architecture for data management. User privacy is compromised in these networks due to the increasing demands and service provider’s services. To ensure the data privacy, the centralized architectures are used without privacy regulations. In this paper, we present a Blockchain-based Privacy-Preserving Authentication (BPPAU) model for ITS networks to ensures users privacy and security. The proposed model provides data storage, data accessing, and processing management by using a blockchain smartcontract system, access control policy and on demand based functions. The proposed model is tested in a simulation environment to check its performance in terms of transaction cost with data size, transaction per second analysis with block time, and computational time analysis with several transactions.
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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 | 0.003 |
| 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 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".