A2E: Attribute-Based Anonymity-Enhanced Authentication for Accessing Driverless Taxi Service
Bibliographic record
Abstract
Driverless taxis (DTs) are gaining attention for their potential to improve urban transportation efficiency. However, unforeseen incidents caused by unsupervised users and the personalized needs of passengers in DTs highlight the need for authenticating user identities and attributes. Additionally, protecting user privacy while enabling rapid traceability of malicious users remains a challenge for the widespread adoption of DTs. This paper proposes a novel Attribute-based Anonymity Enhanced (A2E) authentication scheme for users to access DT services. The security capabilities of A2E include: 1) A2E is attribute-based authentication, which is achieved by designing a user attribute credential. Meanwhile, this attribute credential also satisfies unlinkability. And 2) A2E has enhanced anonymity, which is achieved by designing a decentralized credential issuance mechanism, safeguarding user attributes from association with anonymous identities. Moreover, this mechanism provides traceability and non-frameability to users. From the performance aspect, A2E causes low overhead when tracing malicious users and updating credentials. Besides, both scalability and lightweight are satisfied, which contributes to A2E’s practicability. We conduct security and performance analysis to validate these capabilities.
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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) | 0.000 | 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".