We introduce two mathematical frameworks for foolability in the context of generative distribution learning. In a nuthsell, fooling is an algorithmic task in which the input sample is drawn from some target distribution and the goal is to output a synthetic distribution that is indistinguishable from the target w.r.t to some fixed class of tests. This framework received considerable attention in the context of Generative Adversarial Networks (GANs), a recently proposed approach which achieves impressive empirical results. From a theoretical viewpoint this problem seems difficult to model. This is due to the fact that in its basic form, the notion of foolability is susceptible to a type of overfitting called memorizing. This raises a challenge of devising notions and definitions that separate between fooling algorithms that generate new synthetic data vs. algorithms that merely memorize or copy the training set. The first model we consider is called GAM--Foolability and is inspired by GANs. Here the learner has only an indirect access to the target distribution via a discriminator. The second model, called DP--Foolability, exploits the notion of differential privacy as a candidate criterion for non-memorization. We proceed to characterize foolability within these two models and study their interrelations. We show that DP--Foolability implies GAM--Foolability and prove partial results with respect to the converse. It remains, though, an open question whether GAM--Foolability implies DP--Foolability. We also present an application in the context of differentially private PAC learning. We show that from a statistical perspective, for any class H, learnability by a private proper learner is equivalent to the existence of a private sanitizer for H. This can be seen as an analogue of the equivalence between uniform convergence and learnability in classical PAC learning.