  We present a generic framework for creating differentially private versions
of any hypothesis test in a black-box way. We analyze the resulting tests
analytically and experimentally. Most crucially, we show good practical
performance for small data sets, showing that at epsilon = 1 we only need 5-6
times as much data as in the fully public setting. We compare our work to the
one existing framework of this type, as well as to several
individually-designed private hypothesis tests. Our framework is higher power
than other generic solutions and at least competitive with (and often better
than) individually-designed tests.
