The Achilles’ Heel of License Plate Recognition Parking Enforcement: Balancing Privacy Protection and Enforcement
Bibliographic record
Abstract
Parking enforcement is crucial for addressing illegal parking in urban areas. In smart cities, the license plate recognition (LPR) systems have been adopted to enhance parking enforcement by enabling automated monitoring and detection of parking violations. However, the extensive information collection raises public privacy concerns about how the data are processed and stored on a central server. To address the privacy issue during parking enforcement and enable flexible data access control with the user consent, we propose a novel privacy-preserving and access-control-enhanced parking enforcement scheme, where the central server cannot obtain the license plate information of vehicles that follow the parking rules and can provide encrypted evidence for detected violations in case of disputes. Specifically, by utilizing the keyed-hash message authentication code, parking enforcement vehicles can generate a parking record based on the location and the license plate number of a vehicle, which is then used to identify whether there is a parking violation for the vehicle. Moreover, by integrating the designed time-based conditional proxy re-encryption scheme, the distributed key generation technique, and the blockchain technology, a central server can provide encrypted and tamper-proof evidence for violations. The evidence can only be decrypted by the corresponding vehicle owners (VOs), and the owners can grant the decryption permission to a judge when there is a dispute. The security analysis demonstrates that our scheme can achieve the privacy preservation of VOs and consent-based data access control. Simulation results show the efficiency and practicability of the proposed scheme.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".