Countless papers study the properties of k-wise independent distributions, which are distributions where any k bits are uniform and independent. One property of interest is which computational models are fooled by such distributions, in the sense that they cannot distinguish any such distribution from a uniformly random one. Recently, Bazzi’s breakthrough, discussed earlier on this blog, shows that k = polylog(n) independence fools any polynomial-size DNF on n bits.

Let us change the question. Let us say that instead of one distribution we have two, and we know that any k bits are distributed identically, but not necessarily uniformly. We call such distributions k-wise indistinguishable. (Bounded independence is the special case when one distribution is uniform.) Can a DNF distinguish the two distributions? In fact, what about a single Or gate?

This is the question that we address in a paper with Bogdanov, Ishai, and Williamson. A big thank you goes to my student Chin Ho Lee for connecting researchers who were working on the same problems on different continents. Here at NEU the question was asked to me by my neighbor Daniel Wichs.

The question turns out to be equivalent to threshold/approximate degree, an influential complexity measure that goes back to the works by Minsky and Papert and by Nisan and Szegedy. The equivalence is a good example of the usefulness of duality theory, and is as follows. For any boolean function f on n bits the following two are equivalent:

1. There exist two k-wise indistinguishable distributions that f tells apart with advantage e;

2. No degree-k real polynomial can approximate f to pointwise error at most e/2.

I have always liked this equivalence, but at times I felt slightly worried that could be considered too “simple.” But hey, I hope my co-authors don’t mind if I disclose that it’s been four different conferences, and not one reviewer filed a complaint about that.

From the body of works on approximate degree one readily sees that bounded indistinguishability behaves very differently from bounded independence. For example, one needs k = Ω(√ n) to fool an Or gate, and that is tight. Yes, to spell this out, there exist two distributions which are 0.001 √ n indistinguishable but Or tells them apart with probability 0.999. But obviously even constant independence fools Or.

The biggest gap is achieved by the Majority function: constant independence suffices, by this, while linear indistinguishability is required by Paturi’s lower bound.

In the paper we apply this equivalence in various settings, and here I am just going to mention the design of secret-sharing schemes. Previous schemes like Shamir’s required the computation of things like parity, while the new schemes use different types of functions, for example of constant depth. Here we also rely on the amazing ability of constant-depth circuits to sample distributions, also pointed out earlier on this blog, and apply some expander tricks to trade alphabet size for other parameters.