PriParkRec: Privacy-Preserving Decentralized Parking Recommendation Service
Bibliographic record
Abstract
Insufficient parking space and traffic congestion are important usual suspects in our urban life. Organisation and managing available parking spaces have raised a lot of awareness. Traditional centralized parking approaches are with an insidious single point of failure and unsuitable for large organization because the manager is overburdened with authority and responsibility. The malicious service provider enables to (i) track the precise mobility patterns of citizens. (ii) collect the data to infer privacy-sensitive information, e.g., where the parking requester (i.e., driver) socialize, work, and live; (iii) analyze trajectories to monitor the location of the driver for entertainment (e.g., discovering one-night stands). To design a decentralized smart and practical parking platform without compromising the privacy of the entities is an urgent issue for the smart city. Blockchain (or distributed public ledger technology) is a near-ideal decentralized and distributed solution for centralized parking-space-recommendation systems. However, most of the existing parking recommendation solutions on top of blockchain ignore to protect the privacy of drivers during parking spot detection and matching phases while cannot maintain the property of anonymity of the drivers. In this paper, an efficient and privacy-preserving parking-space recommendation service platform, named as PriParkRec, along with the proof-of-concept solution to protect the requester's privacy in PriParkRec while maintaining most of the benefits of current parking-space sharing service, i.e., accountability, (anonymous) authentication, (anonymous) payment, and reputation ratings. Indeed, PriParkRec service relies on well-known privacy-enhancing cryptographic building blocks (e.g., oblivious PRF, private set intersection, anonymous credentials, anonymous payment, and smart-contract, etc), these building blocks are carefully integrated together to achieve privacy and security goals to revolutionize the existing car-parking service system coming with the private blockchain and industrial internet of things (IIoTs) technologies.
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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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".