Preserving Privacy in the Internet of Connected Vehicles

Ghane, S; Jolfaei, A; Kulik, L; Ramamohanarao, K; Puthal, D

Preserving Privacy in the Internet of Connected Vehicles

Ghane, S Jolfaei, A Kulik, L Ramamohanarao, K Puthal, D

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2021, 22, (8), pp. 5018-5027
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Ghane, S
dc.contributor.author	Jolfaei, A
dc.contributor.author	Kulik, L
dc.contributor.author	Ramamohanarao, K
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X
dc.date.accessioned	2022-04-14T03:20:32Z
dc.date.available	2022-04-14T03:20:32Z
dc.date.issued	2021-08-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2021, 22, (8), pp. 5018-5027
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/156248
dc.description.abstract	Today's vehicles are advancing from stand-alone transportation means to vehicle-to-vehicle, and vehicle-to-infrastructure communications enabled devices which are able to exchange data through the transportation communication infrastructure. As the IoT and data remain intrinsically linked together, the fast-changing mobility landscape of intent-based networking for the Internet of connected vehicles comes with a great risk of data security and privacy violations. This paper considers the privacy issues in the distributed edge computing, in which the data is communicated between a number of vehicles in the IoT layer and potentially untrusted edge controllers at the edge of the network. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the users' privacy if they are leaked with no perturbation. Recent studies suggest mechanisms for randomizing the stream of data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or there is no correlation between the users or the featured of sensory data. In this paper, we address this gap by proposing a differentially private data streaming system that adds a correlated noise in the vehicle's side (IoT layer) rather than the transportation infrastructure. Also, our system is able to ensure a strong privacy level over time. The proposed mechanism is data-adaptive and scales the noise with respect to the data correlation. Our extensive experiments demonstrate that the utility of the output generated by our method outperforms the recent approaches.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2020.2964410
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.title	Preserving Privacy in the Internet of Connected Vehicles
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-14T03:20:30Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	8

Abstract:

Today's vehicles are advancing from stand-alone transportation means to vehicle-to-vehicle, and vehicle-to-infrastructure communications enabled devices which are able to exchange data through the transportation communication infrastructure. As the IoT and data remain intrinsically linked together, the fast-changing mobility landscape of intent-based networking for the Internet of connected vehicles comes with a great risk of data security and privacy violations. This paper considers the privacy issues in the distributed edge computing, in which the data is communicated between a number of vehicles in the IoT layer and potentially untrusted edge controllers at the edge of the network. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the users' privacy if they are leaked with no perturbation. Recent studies suggest mechanisms for randomizing the stream of data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or there is no correlation between the users or the featured of sensory data. In this paper, we address this gap by proposing a differentially private data streaming system that adds a correlated noise in the vehicle's side (IoT layer) rather than the transportation infrastructure. Also, our system is able to ensure a strong privacy level over time. The proposed mechanism is data-adaptive and scales the noise with respect to the data correlation. Our extensive experiments demonstrate that the utility of the output generated by our method outperforms the recent approaches.

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http://hdl.handle.net/10453/156248