Abstract
In this presentation I will discuss recent work using additive perturbations from elliptical distributions to achieve differential privacy. Examples of elliptical distributions include both the Laplace and Gaussian, but many other options are available that achieve different tail behaviors. I will also provide a seemingly odd result showing that while elliptical distributions can achieve epsilon privacy in finite dimensions, it is impossible in infinite dimensions; elliptical distributions in infinite dimensions can only achieve (epsilon, delta) privacy.