DAPA: A Decentralized, Accountable, and Privacy-Preserving Architecture for Car Sharing Services
Bibliographic record
Abstract
Car sharing offers a flexible peer-to-peer or station based car rental service to customers. On one hand, it requires customers to expose identifications (e.g., valid driving licenses) to car sharing service providers (CSSPs) for accountability, i.e., the driving qualification of customers can be verified and misbehaving customers can be traced by CSSPs. On the other hand, privacy concerns arise when customers identities are exposed as honest-but-curious CSSPs may secretly extract customers privacy information by linking their car rental records to their identities. To resolve this contradiction, we propose a decentralized, accountable, and privacy-preserving architecture for car sharing services, named DAPA. In specific, to overcome the limitation of the single point of failure, multiple dynamic validation servers are employed to substitute a single trusted third-party authority and assist in building decentralized trust for customers. In addition, to protect customers' privacy and achieve accountability simultaneously under the decentralized architecture, a new privacy-preserving identity management (PPIM) scheme is introduced as a basic module for DAPA. Customers' identities are protected in a distributed and dynamic manner but publicly verified based on a well-designed zero-knowledge proof protocol. Only the misbehaving customers' identities can be recovered by a majority of validation servers using adaptive verifiable secret sharing/redistribution techniques. Detailed security analysis shows that DAPA can minimize privacy breaches and guarantee the accountability. Performance evaluations via extensive simulations demonstrate that DAPA is efficient in terms of computational costs and communication overheads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".