Toward Lightweight and Privacy-Preserving Data Provision in Digital Forensics for Driverless Taxi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data provision, referring to data upload and data access, is one key phase in vehicular digital forensics. The unique features of driverless taxi (DT) bring new issues to this phase: I1) efficient verification of data integrity when diverse data providers (DPs) upload data; I2) DP privacy preservation during data upload; and I3) privacy preservation of both data and investigator (IN) under complex data ownership when accessing data. Considering that the existing works on digital forensics cannot address all these issues, we first propose a novel lightweight and privacy-preserving data provision (LPDP) approach consisting of three mechanisms: 1) privacy-friendly batch verification mechanism (PBVm); 2) data access control mechanism (DACm); and 3) decentralized IN warrant issuance mechanism (DIWIm). PBVm ensures scalable verification of data integrity to address I1. PBVm also ensures the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests to address I2. Besides, DACm and DIWIm are combined to ensure data privacy preservation and the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability to address I3. Security analysis and performance evaluations validate LPDP’s capabilities in addressing the three issues.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| 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 it