Can Differential Privacy be used to show that two distributions are indistinguishable? Differential privacy can be used to show that the &quot;privacy loss&quot; of a certain computation is &quot;bounded&quot; in a meaningful way.
In cryptography, often &quot;indistinguishability&quot; 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 &quot;concentrated differential privacy&quot;, 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 &quot;So, the two distributions are indistinguishable&quot; --- can differential privacy help me with this goal?
These sorts of arguments come up in the theory of randomness extractors where we argue that a random function from a certain family when applied to inputs with a certain minimum entropy ($H_\infty$, the -ve log-maximum probability of an outcome in the Renyi notation) produce outputs that are indistinguishable from uniform up to some $\epsilon$ bound. The differential privacy of the uniform and extractor distributions can be bounded in terms of input size, output size and $H_\infty$ (see for example the Kamp and Zuckerman theorem of the article).
The min-entropy should be the choice of entropy function when using differential privacy as it bounds the worst case scenario of concentration of inputs.