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
VPriv: Protecting Privacy in Location-Based Vehicular Services.
Raluca Ada Popa, Hari Balakrishnan, and Andrew J. Blumberg.
In the proceedings of the 18th Usenix Security Symposium, Montreal, Canada, 2009.
Privacy and Accountability for Location-Based Aggregate Statistics.
Raluca Ada Popa, Andrew J. Blumberg, Hari Balakrishnan, and Frank H. Li.
In the Proceedings of 18th ACM Conference on Computer and Communications Security (CCS'11), Chicago, 2011.
[paper] [extended paper]