Vpriv: Protecting Privacy in Location-Based Vehicular Services

Abstract

A variety of location-based vehicular services are currently being woven into the national transportation infrastructure in many countries. These include usage- or congestion-based road pricing, traffic law enforcement, traffic monitoring, “pay-as-you-go” insurance, and vehicle safety systems. Although such applications promise clear benefits, there are significant potential violations of the location privacy of drivers under standard implementations (i.e., GPS monitoring of cars as they drive, surveillance cameras, and toll transponders). In this paper, we develop and evaluate VPriv, a system that can be used by several such applications without violating the location privacy of drivers. The starting point is the observation that in many applications, some centralized server needs to compute a function of a user’s path—a list of time-position tuples. VPriv provides two components: 1) the first practical protocol to compute path functions for various kinds of tolling, speed and delay estimation, and insurance calculations in a way that does not reveal anything more than the result of the function to the server, and 2) an out-of-band enforcement mechanism using random spot checks that allows the server and application to handle misbehaving users. Our implementation and experimental evaluation of VPriv shows that a modest infrastructure of a few multi-core PCs can easily serve 1 million cars. Using analysis and simulation based on real vehicular data collected over one year from the CarTel project’s testbed of 27 taxis running in the Boston area, we demonstrate that VPriv is resistant to a range of possible attacks.