  In order to better manage the premiums and encourage safe driving, many
commercial insurance companies (e.g., Geico, Progressive) are providing options
for their customers to install sensors on their vehicles which collect
individual vehicle's traveling data. The driver's insurance is linked to
his/her driving behavior. At the other end, through analyzing the historical
traveling data from a large number of vehicles, the insurance company could
build a classifier to predict a new driver's driving style: aggressive or
defensive. However, collection of such vehicle traveling data explicitly
breaches the drivers' personal privacy. To tackle such privacy concerns, this
paper presents a privacy-preserving driving style recognition technique to
securely predict aggressive and defensive drivers for the insurance company
without compromising the privacy of all the participating parties. The
insurance company cannot learn any private information from the vehicles, and
vice-versa. Finally, the effectiveness and efficiency of the privacy-preserving
driving style recognition technique are validated with experimental results.
