This tool quantifies location privacy, for various location-based applications and location-privacy preserving mechanisms (LPPMs). It quantifies location privacy in an adversarial framework, based on the inference error of the attacker. It enables constructing the background knowledge of the adversary, as the mobility model of users, and evaluates the risks of various inference attacks, notably identification and reconstruction attacks. The tool is designed based on the formal framework proposed in the following papers:
- 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.
- 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.
The tool is designed and developed by Vincent Bindschaedler and Reza Shokri.
Please read the Quick Starte Guide for learning how to use the library.