BSFP: Blockchain-Enabled Smart Parking With Fairness, Reliability and Privacy Protection
Bibliographic record
Abstract
The convenience of using private cars has an accompanying parking challenge which becomes a significant issue in congested metropolitans and downtown areas. The explosive increase in the number of vehicles has substantially raised the issue of finding a suitable parking spot, which is both time and resource consuming. At the same time, many private parking spots remain idle, while their owners are not present at home. To promote the utility of private parking spots and mitigate parking issues, smart parking apps can be used. Unfortunately, some of them suffer from privacy issues that affect participation willingness, while others work in a centralized environment where the availability of service is not guaranteed in the presence of malicious users. In this work, we propose Blockchain-based Smart parking with Fairness, reliability and Privacy protection, called BSFP. Specifically, group signatures, bloom filters, and vector-based encryption are leveraged to protect the user's privacy. The decentralized nature of blockchain is utilized to achieve reliability in smart parking, and the smart contract is used to realize fairness. Comprehensive security analysis and experimental results based on the real-world dataset show that BSFP achieves fairness, reliability and privacy protection with high efficiency.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".