The Achilles’ Heel of License Plate Recognition Parking Enforcement: Balancing Privacy Protection and Enforcement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it