VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility
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
With the rapidly growing deployment of intelligent transportation systems (ITS) and smart traffic applications, vehicle trajectory data are ubiquitously generated, e.g., from GPS navigation systems, mobile applications, and urban traffic cameras. Analyzing such fine-grained data would greatly benefit the development of ITS and smart cities, yet pose severe privacy risks due to the recorded drivers’ visited locations, routes, and driving habits. Recently, some privacy enhancing techniques were proposed to sanitize such data. However, such schemes have some major limitations–they either lack formal privacy notions to quantify and bound the privacy risks, or result in very limited utility, e.g., only a sequence of locations or aggregated information can be released (without retaining the speeds, accelerations and the timestamps of vehicles). In this article, we propose a novel framework to sanitize the fine-grained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vehicle trajectories with differential privacy</i> (VTDP), which provides rigorous privacy protection against adversaries who possess arbitrary background knowledge. Our VTDP technique involves three phases of differentially private sampling, which sequentially generate all the three categories of data (besides a pseudo identity for each vehicle)– <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">position, moving,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timestamps</i> . It also includes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vehicle trajectory interpolation</i> procedure to further improve the output utility with the properties of fine-grained vehicle trajectory data. We conducted experiments on real vehicle trajectory datasets to validate the performance of our approach.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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