  We present a particle-based framework for estimating the position of a
vehicle using map information and measurements of speed. Two measurement
functions are considered. The first is based on the assumption that the lateral
force on the vehicle does not exceed critical limits derived from physical
constraints. The second is based on the assumption that the driver approaches a
target speed derived from the speed limits along the upcoming trajectory.
Performance evaluations of the proposed method indicate that end destinations
often can be estimated with an accuracy in the order of $100\,[m]$. These
results expose the sensitivity and commercial value of data collected in many
of today's insurance telematics programs, and thereby have privacy implications
for millions of policyholders. We end by discussing the strengths and
weaknesses of different methods for anonymization and privacy preservation in
telematics programs.
