Trajectory Obfuscation and Detection in Internet-of-vehicles

Zhao, Y; Ma, B; Wang, Z; Liu, Z; Zeng, Y; Ma, J

Trajectory Obfuscation and Detection in Internet-of-vehicles

Zhao, Y Ma, B

Wang, Z Liu, Z Zeng, Y Ma, J

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, 2022, 00, pp. 769-774
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	Trajectory Obfuscation and Detection in Internet-of-vehicles.pdf	Published version	2.88 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhao, Y
dc.contributor.author	Ma, B https://orcid.org/0000-0003-4167-2797
dc.contributor.author	Wang, Z
dc.contributor.author	Liu, Z
dc.contributor.author	Zeng, Y
dc.contributor.author	Ma, J
dc.date	2022-05-04
dc.date.accessioned	2023-04-11T00:26:15Z
dc.date.available	2023-04-11T00:26:15Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, 2022, 00, pp. 769-774
dc.identifier.isbn	9781665405270
dc.identifier.uri	http://hdl.handle.net/10453/169471
dc.description.abstract	In Internet-of-vehicles, vehicles cooperate with each other by transmitting Internet-of-vehicles and location-based service (LBS) providers optimize services by analyzing trajectory data collected from drivers. Nevertheless, illegal trajectories generated by attackers or malicious drivers can obfuscate the process of analysis and breach the quality of service. Some mechanisms protect drivers' location privacy by using obfuscation-based schemes. Obfuscation-based mechanisms report LBS with obfuscated trajectories data rather than actual trajectories, which increases difficulties to detect illegal trajectories accurately. This paper focuses on detecting illegal trajectories when all drivers employ obfuscation-based mechanisms to protect location privacy. In this paper, we propose a dynamic obfuscation mechanism in road networks based on Geo-indistinguishability to dynamically protect drivers' location privacy. Considering personalization in road networks, we also propose a classification mechanism to detect illegal trajectories in road networks. Illegal trajectories are generated based on real trajectories to simulate actions of malicious drivers and attackers. Experiment results in real road networks show that the classifier can detect illegal obfuscated trajectories with at least 94% Area Under the Curve (AUC) score, which outperforms than existing works in road networks.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022
dc.relation.ispartof	2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
dc.relation.isbasedon	10.1109/CSCWD54268.2022.9776163
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Trajectory Obfuscation and Detection in Internet-of-vehicles
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-11T00:26:13Z
pubs.finish-date	2022-05-06
pubs.publication-status	Published
pubs.start-date	2022-05-04
pubs.volume	00

Abstract:

In Internet-of-vehicles, vehicles cooperate with each other by transmitting Internet-of-vehicles and location-based service (LBS) providers optimize services by analyzing trajectory data collected from drivers. Nevertheless, illegal trajectories generated by attackers or malicious drivers can obfuscate the process of analysis and breach the quality of service. Some mechanisms protect drivers' location privacy by using obfuscation-based schemes. Obfuscation-based mechanisms report LBS with obfuscated trajectories data rather than actual trajectories, which increases difficulties to detect illegal trajectories accurately. This paper focuses on detecting illegal trajectories when all drivers employ obfuscation-based mechanisms to protect location privacy. In this paper, we propose a dynamic obfuscation mechanism in road networks based on Geo-indistinguishability to dynamically protect drivers' location privacy. Considering personalization in road networks, we also propose a classification mechanism to detect illegal trajectories in road networks. Illegal trajectories are generated based on real trajectories to simulate actions of malicious drivers and attackers. Experiment results in real road networks show that the classifier can detect illegal obfuscated trajectories with at least 94% Area Under the Curve (AUC) score, which outperforms than existing works in road networks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169471