I will explore some recent advances (as well as open problems) in understanding the sample complexity (and computational complexity) of learning Gaussians and their mixtures under (approximate) differential privacy. The canonical problem here is sample-efficient, polytime, and private learning of high-dimensional Gaussians. Another related problem is private learning GMMs. This will be a relatively high-level talk with discussion of the open problems in the field.