LPM2  version 0.14a
Location-Privacy and Mobility Meter
Location-Privacy and Mobility Meter (LPM2):
A Tool to Model Human Mobility and Quantify Location Privacy

   Reza Shokri contact, Vincent Bindschaedler, George Theodorakopoulos, George Danezis, Jean-Pierre Hubaux, Jean-Yves Le Boudec

The location privacy of mobile users of location-based services and the success of an adversary in his location-inference attacks on the users' queries are two sides of the same coin. We rely on well-established statistical methods (such as Bayesian inference, hidden Markov model, and Markov-Chain Monte-Carlo methods) to formalize and implement the location inference attacks in a tool: the Location-Privacy Meter that quantifies the location privacy of mobile users, given various location-based applications and location-privacy preserving mechanisms (LPPMs). We use the expected inference error of the adversary as the location privacy metric in this framework. The tool is written in C++ and is designed to be used as a static library. New LPPMs can be imported into the tool and their effectiveness on some location traces can be evaluated using the Location-Privacy Meter.

The tool is designed based on the formal framework proposed in the following papers:

Reza Shokri, George Theodorakopoulos, George Danezis, Jean-Pierre Hubaux, and Jean-Yves Le Boudec.
Quantifying Location Privacy: The Case of Sporadic Location Exposure.
In The 11th Privacy Enhancing Technologies Symposium (PETS), Waterloo, July 2011.

Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux.
Quantifying Location Privacy.
In IEEE Symposium on Security and Privacy (S&P), Oakland, May 2011.


Download Quick Start Guide

Download Binaries v0.14a (Release date: 9 July 2012) with the example Codes: x86, x64

Download Binaries (debug) v0.14a (Release date: 9 July 2012) with the example Codes: x86, x64

Download Source Code v0.14a (Release date: 9 July 2012)



 All Classes Files Functions