Evaluating Trajectory Privacy in Autonomous Vehicular Communications Oakland University

Format:

Book

Conference/Event

Author/Creator:

Banihani, Banihani, author.

Contributor:

Alzahrani, Abdulrahman

Corser, George P.

Fu, Huirong

Zaiter, Abdulrahman

Conference Name:

WCX SAE World Congress Experience (2019-04-09 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2019

Summary:

AbstractAutonomous 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

Notes:

Vendor supplied data

Publisher Number:

2019-01-0487

Access Restriction:

Restricted for use by site license