Title: Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing,
Abstract: Advances in technology have provided ways to monitor and measure driving
behavior. Recently, this technology has been applied to usage-based automotive
insurance policies that offer reduced insurance premiums to policy holders who
opt-in to automotive monitoring. Several companies claim to measure only speed
data, which they further claim preserves privacy. However, we have developed an
algorithm - elastic pathing - that successfully tracks drivers' locations from
speed data. The algorithm tracks drivers by assuming a start position, such as
the driver's home address (which is typically known to insurance companies),
and then estimates the possible routes by fitting the speed data to map data.
To demonstrate the algorithm's real-world applicability, we evaluated its
performance with driving datasets from central New Jersey and Seattle,
Washington, representing suburban and urban areas. We are able to estimate
destinations with error within 250 meters for 17% of the traces and within 500
meters for 24% of the traces in the New Jersey dataset, and with error within
250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the
Seattle dataset. Our work shows that these insurance schemes enable a
substantial breach of privacy.