  Hypothesis tests are a crucial statistical tool for data mining and are the
workhorse of scientific research in many fields. Here we present a
differentially private analogue of the classic Wilcoxon signed-rank hypothesis
test, which is used when comparing sets of paired (e.g., before-and-after) data
values. We present not only a private estimate of the test statistic, but a
method to accurately compute a p-value and assess statistical significance. We
evaluate our test on both simulated and real data. Compared to the only
existing private test for this situation, that of Task and Clifton, we find
that our test requires less than half as much data to achieve the same
statistical power.

Cryptography and Security;Machine Learning;Machine Learning