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

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.

paper slides

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.

conference paper extended paper

Code

VPriv: C++ crypto protocol here.

Maintained by Raluca Ada Popa (raluca AT csail DOT mit DOT edu)