Evaluating Trajectory Privacy in Autonomous Vehicular Communications

2019-01-0487

04/02/2019

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.
Meta TagsDetails
DOI
https://doi.org/10.4271/2019-01-0487
Pages
8
Citation
Banihani, A., Zaiter, A., Corser, G., Fu, H. et al., "Evaluating Trajectory Privacy in Autonomous Vehicular Communications," SAE Technical Paper 2019-01-0487, 2019, https://doi.org/10.4271/2019-01-0487.
Additional Details
Publisher
Published
Apr 2, 2019
Product Code
2019-01-0487
Content Type
Technical Paper
Language
English