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Using Map Matching for DeIdentification of Connected Vehicle Locations

by Jason M Carter, Aaron E Ferber
Publication Type
Journal
Journal Name
IEEE Consumer Electronics Magazine
Publication Date
Page Numbers
111 to 116
Volume
8
Issue
6

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-critical applications that require precise position information. Furthermore, k-anonymity-based clustering approaches would lead to significant data suppression. Our algorithm attempts to balance privacy protection and data utility, while protecting against re-identification attacks. Our data is from the first connected vehicle model deployment in the United States.