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.

通过使用分层方法，可以在不用额外的隐私预算的情况下得到高准确性的全局统计学估计，从而减少差异的工作是减少差异的差异数据隐私机制应该被比较的一个强有力的基线。

一个简单实用的方法来减少差异隐私的不均衡影响

A Simple and Practical Method for Reducing the Disparate Impact of  Differential Privacy

We study the accuracy of differentially private mechanisms in the continual
release model. A continual release mechanism receives a sensitive dataset as a
stream of $T$ inputs and produces, after receiving each input, an accurate
output on the obtained inputs. In contrast, a batch algorithm receives the data
as one batch and produces a single output.
We provide the first strong lower bounds on the error of continual release
mechanisms. In particular, for two fundamental problems that are widely studied
and used in the batch model, we show that the worst case error of every
continual release algorithm is $\tilde \Omega(T^{1/3})$ times larger than that
of the best batch algorithm. Previous work shows only a polylogarithimic (in
$T$) gap between the worst case error achievable in these two models; further,
for many problems, including the summation of binary attributes, the
polylogarithmic gap is tight (Dwork et al., 2010; Chan et al., 2010). Our
results show that problems closely related to summation -- specifically, those
that require selecting the largest of a set of sums -- are fundamentally harder
in the continual release model than in the batch model.
Our lower bounds assume only that privacy holds for streams fixed in advance
(the "nonadaptive" setting). However, we provide matching upper bounds that
hold in a model where privacy is required even for adaptively selected streams.
This model may be of independent interest.

研究差分隐私机制在连续发布模型中的准确性并对一些基本问题的批处理模型下的最优性做了界限，发现连续发布算法的最坏情况误差是最佳批处理算法的大约 T 的三分之一倍，揭示了连续发布模型中涉及求和等问题的困难性。