Publications
Showing 32 results for Author: Jason M. Carter
Nov, 2023
Journal
Using Map Matching for DeIdentification of Connected Vehicle Locations
We introduce a location deidentification procedure that uses road network structure to protect against certain types of inference-based attacks. Our target is large databases containing vehicle locations. Previous anonymization approaches are inappropriate because location generalization and perturbation of geopositions could negatively affect development of safety-critica…
Sep, 2016
Conference Paper
Towards a Scalable Group Vehicle-based Security System
In August 2014, the National Highway Traffic Safety Administration (NHTSA) proposed new rulemaking to require V2V communication in light vehicles. To establish trust in the basic safety messages (BSMs) that are exchanged by vehicles to improve driver safety, a vehicle public key infrastructure (VPKI) is required. We outline a system where a group or groups of vehicles mana…
Sep, 2015
Conference Paper
Analysis of Vehicle-Based Security Operations
Vehicle-to-vehicle (V2V) communications promises to increase roadway safety by providing each vehicle with 360 degree situational awareness of other vehicles in proximity, and by complementing onboard sensors such as radar or camera in detecting imminent crash scenarios. In the United States, approximately three hundred million automobiles could participate in a fully depl…
Apr, 2015
ORNL Report
Connected Vehicle Privacy Assessment – Geotrack De-Identification Algorithm
Global positioning technology has become integrated into our daily lives in a way no one would have imagined a generation ago. Intelligent transportation systems will use the precise position information this technology furnishes to save lives. Positioning technology provides undeniable benefits; however, it can be exploited to learn the movements of individuals. Examinati…
Jul, 2014
ORNL Report
Connected Vehicle Data Privacy Assessment – Leesburg Research Data Exchange Dataset
This document outlines an attempt to re-identify the driver, vehicle, and vehicle owner that generated the Basic Safety Messages (BSM) contained in the Research Data Exchange Leesburg dataset. Seven different analysis techniques were explored, and publicly available web-based search tools were used to gather the auxiliary information necessary for the re-identification tas…
Jul, 2014
ORNL Report
Connected Vehicle Data Privacy Assessment – Personally Identifiable Information Analysis of Research Data Exchange Data
The goal of this portion of the Connected Vehicle Data Privacy Assessment was to ensure the existing RDE is as free as possible of data artifacts that could expose the identity of an individual driver or that driver’s vehicle; it also aided the development of more effective de-identification algorithms. Nine data environments were reviewed and several very specific ways to…