Differential privacy can be used to show that the "privacy loss" of a certain computation is "bounded" in a meaningful way. In cryptography, often "indistinguishability" is considered, i.e. we want $\Delta(D_0, D_1)$ to be small. Can these two concepts be related?
I know vaguely that standard notions of differential privacy can be related to things like "concentrated differential privacy", which itself is a Renyi-type bound on two distributions (which is somewhat similar to the total variation distance bound that I want). But I want to end my proof with "So, the two distributions are indistinguishable" --- can differential privacy help me with this goal?