Applying the IEEE Digital Privacy Model to Connected Vehicles and Intelligent Transportation Systems
Bibliographic record
Abstract
Designers and operators of Intelligent Transportation Systems have long acknowledged the need to ensure that the privacy rights of the public be protected. The highly personal nature of transportation has guaranteed close scrutiny by public interest advocacy groups. Despite the adoption of longstanding approaches such as Privacy by Design by vendors, vehicle manufacturers, and road authorities, many transportation stakeholders continue to express concerns, as evidenced by the many critical articles and white papers that have been published during the past decade. Traditional approaches to framing privacy issues have tended to emphasize technical methods to ensure that data concerning individuals is anonymized based upon abstract notions of privacy rights. In light of the apparent weaknesses of this approach, the IEEE Digital Privacy Initiative has taken a user-centric approach that adopts a dual focus on the user’s expectations of privacy and the factors that influence whether privacy is achieved. Here, we: 1) explore the application of the IEEE Digital Privacy Model as an analytical tool for understanding the root causes of privacy disputes in connected vehicle ecosystems and intelligent transportation systems and 2) demonstrate its effectiveness over traditional Privacy by Design approaches through practical implementations with municipal and provincial road authorities across Canada.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".