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Advances in technology have provided ways to monitor and measure drivingbehavior. Recently, this technology has been applied to usage-based automotiveinsurance policies that offer reduced insurance premiums to policy holders whoopt-in to automotive monitoring. Several companies claim to measure only speeddata, which they further claim preserves privacy. However, we have developed analgorithm - elastic pathing - that successfully tracks drivers' locations fromspeed data. The algorithm tracks drivers by assuming a start position, such asthe 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 itsperformance with driving datasets from central New Jersey and Seattle,Washington, representing suburban and urban areas. We are able to estimatedestinations with error within 250 meters for 17% of the traces and within 500meters for 24% of the traces in the New Jersey dataset, and with error within250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in theSeattle dataset. Our work shows that these insurance schemes enable asubstantial breach of privacy.

Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing