| Title | Preserving Trajectory Privacy in Driving Data Release |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Xu, Yi, Wang, Chong Xiao, Song, Yang, Tay, Wee Peng |
| Conference Name | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Keywords | composability, compositionality, data privacy, Data Sanitization, Deep Learning, Driver behavior detection, pubcrawl, Real-time Systems, resilience, Resiliency, road safety, Servers, Signal processing, Trajectory, trajectory privacy |
| Abstract | Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle's trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks. |
| Notes | ISSN: 2379-190X |
| DOI | 10.1109/ICASSP43922.2022.9746677 |
| Citation Key | xu_preserving_2022 |
Preserving Trajectory Privacy in Driving Data Release