Not All Learnable Distribution Classes are Privately Learnable

We give an example of a class of distributions that is learnable up to constant error in total variation distance with a finite number of samples, but not learnable under $(\varepsilon, \delta)$-differential privacy with the same target error. This weakly refutes a conjecture of Ashtiani.