Location-Privacy Meter: A Tool to Quantify Location Privacy
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.
 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, Canada, July 27–29, 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, CA, USA, May 22-25, 2011.