A Simple and Practical Method for Reducing the Disparate Impact of
  Differential Privacy
Lucas Rosenblatt Julia Stoyanovich Christopher Musco
Differentially private (DP) mechanisms have been deployed in a variety of
high-impact social settings (perhaps most notably by the U.S. Census). Since
all DP mechanisms involve adding noise to results of statistical queries, they
are expected to impact our ability to accurately analyze and learn from data,
in effect trading off privacy with utility. Alarmingly, the impact of DP on
utility can vary significantly among different sub-populations. A simple way to
reduce this disparity is with stratification. First compute an independent
private estimate for each group in the data set (which may be the intersection
of several protected classes), then, to compute estimates of global statistics,
appropriately recombine these group estimates. Our main observation is that
naive stratification often yields high-accuracy estimates of population-level
statistics, without the need for additional privacy budget. We support this
observation theoretically and empirically. Our theoretical results center on
the private mean estimation problem, while our empirical results center on
extensive experiments on private data synthesis to demonstrate the
effectiveness of stratification on a variety of private mechanisms. Overall, we
argue that this straightforward approach provides a strong baseline against
which future work on reducing utility disparities of DP mechanisms should be
compared.