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 novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>ulti-authorized, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i>rivacy-protected, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i>raceable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i>ata <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i>haring (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.
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.
How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".