Browse by author
Lookup NU author(s): Dr Deepak PuthalORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.
For re-use rights please refer to the publisher's terms and conditions.
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.
Author(s): Ghane S, Jolfaei A, Kulik L, Ramamohanarao K, Puthal D
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Intelligent Transportation Systems
Print publication date: 01/08/2021
Online publication date: 15/01/2020
Acceptance date: 16/12/2019
Date deposited: 11/02/2021
ISSN (print): 1524-9050
ISSN (electronic): 1558-0016
Altmetrics provided by Altmetric