  Sparse histogram methods can be useful for returning differentially private
counts of items in large or infinite histograms, large group-by queries, and
more generally, releasing a set of statistics with sufficient item counts. We
consider the Gaussian version of the sparse histogram mechanism and study the
exact $\epsilon,\delta$ differential privacy guarantees satisfied by this
mechanism. We compare these exact $\epsilon,\delta$ parameters to the simpler
overestimates used in prior work to quantify the impact of their looser privacy
bounds.
