  A new line of work [Dwork et al. STOC 2015], [Hardt and Ullman FOCS 2014],
[Steinke and Ullman COLT 2015], [Bassily et al. STOC 2016] demonstrates how
differential privacy [Dwork et al. TCC 2006] can be used as a mathematical tool
for guaranteeing generalization in adaptive data analysis. Specifically, if a
differentially private analysis is applied on a sample S of i.i.d. examples to
select a low-sensitivity function f, then w.h.p. f(S) is close to its
expectation, although f is being chosen based on the data.
  Very recently, Steinke and Ullman observed that these generalization
guarantees can be used for proving concentration bounds in the non-adaptive
setting, where the low-sensitivity function is fixed beforehand. In particular,
they obtain alternative proofs for classical concentration bounds for
low-sensitivity functions, such as the Chernoff bound and McDiarmid's
Inequality.
  In this work, we set out to examine the situation for functions with
high-sensitivity, for which differential privacy does not imply generalization
guarantees under adaptive analysis. We show that differential privacy can be
used to prove concentration bounds for such functions in the non-adaptive
setting.
