Quantifying Location Privacy [Test of Time Award IEEE S&P 21]

We view location privacy as a statistical inference problem: The adversary makes noisy observations of the user’s location and then tries to infer the actual location. The privacy metric is then the attacker’s inference error. Modelling privacy in this way helps clarify and quantify assumptions about the adversary’s background knowledge, and it helps compare various protection mechanisms. This talk will present this approach, which was published at Oakland 2011 and received the Test of Time Award at Oakland 2021, and it will explore subsequent results in this area.