In this talk, we review the notion of Differential Privacy, its implications in terms of Bayesian Adversary, and we discuss typical implementations from the point of view of their optimality with respect to utility. Then, we consider an extension of differential privacy to general metric domains, and the consequences for the optimality results. Finally, we show an instantiation to the case of location privacy, leading to the notion of geo-indistinguishability.