Location Privacy for Vehicular and Mobile Systems



In recent years, there has been a rapid emergence of location-based mobile services that promise tremendous benefits, but unfortunately violate the privacy of individuals. We developed two systems VPriv and PrivStats that preserve location privacy while maintaining the benefits of such services.

VPriv allows an untrusted server to compute an agreed-upon function on an individual's path without learning his path.  VPriv can be applied to electronic toll collection, traffic delay and average speed estimation, traffic law enforcement, "pay-as-you-go" insurance pricing, and some location-based social applications and statistics.

PrivStats
allows an untrusted server to compute statistics on many people's paths without learning each individual's path. PrivStats can be applied to computing a large class of traffic statistics (e.g., average speed, average delays, congestion estimation, standard deviations)  as well as to some social  applications (e.g., ratings and reviews of  restaurants, popularity of certain locations).

People

Publications