Multi-Authorized, Privacy-Protected, and Traceable Driverless Taxi Data Sharing in Internet of Vehicles
Bibliographic record
Abstract
Advanced wireless communication and artificial intelligence technologies are significantly facilitating the deployment of driverless taxis (DTs). To ensure high-quality of DT services, data sharing among DTs is essential during service provision. However, at least three key challenges are then raised in terms of security and privacy: 1) guaranteeing data authenticity with traceability; 2) enabling flexible data authorization and preserving privacy amid complex data ownership; and 3) protecting DT privacy under frequent data sharing. Most existing works address only one aspect, such as data authenticity, privacy preservation, or single-entity authorization. To address these challenges, we propose a novelMulti-authorized,Privacy-protected, andTraceableDataSharing (MPTDS) scheme for DTs. Firstly, MPTDS upholds data privacy preservation by designing the data sharing credential for each DT and the data access credential for each service provider. Data sharing credential also ensures anonymity and unlinkability of DTs. Secondly, MPTDS ensures data integrity and origin authentication by designing the credential-based authentication technique for DT, which also enables the traceability. Additionally, MPTDS facilitates the data multi-authorization by designing the data sharing credential multi-party issuance technique without sacrificing the non-frameabiltiy. We conduct security analyses and performance evaluations to validate the security properties and practicality of MPTDS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".