论文标题
DIPP：偏见校正的私人倾向分数
DiPPS: Differentially Private Propensity Scores for Bias Correction
论文作者
论文摘要
在调查中，通常由个人决定是否要参加，这导致参与偏见：愿意共享数据的个人可能无法代表整个人群。同样，在某些情况下，人们无法直接访问目标人群的任何数据，并且必须诉诸于从不同分布中采样的公开可用的代理数据。在本文中，我们提出了偏置校正（DIPP）的私人倾向分数，这是一种近似上述两个设置中感兴趣的真实数据分布的方法。我们假设数据分析师可以访问一个数据集$ \ tilde {d} $，该{d} $以偏见的方式从兴趣分布中进行了采样。随着个人在获得隐私保证时可能更愿意共享他们的数据，因此我们进一步假设分析师可以在本地私人访问一组样本$ d $ $ d $中，从真实的，公正的发行版中允许分析师。私有，无偏数据集$ d $的每个数据点都映射到簇上的概率分布（从有偏的数据集$ \ tilde {d} $中学到的概率分布，从中通过指数机制对单个群集进行采样并与数据分析师共享。这样，分析师将分布分布在簇上，他们用来计算有偏见的$ \ tilde {d} $中点的倾向分数，而这些分数又用于将点重新为$ \ tilde {d} $中的点来近似于真实的数据分布。现在可以计算由此产生的重新加权数据集中的任何功能，而无需进一步访问私人$ d $。在来自各个域的数据集的实验中，我们表明DIPPS成功地使可用数据集的分布更接近Wasserstein距离的兴趣分布。我们进一步表明，这导致了不同统计数据的估计值。
In surveys, it is typically up to the individuals to decide if they want to participate or not, which leads to participation bias: the individuals willing to share their data might not be representative of the entire population. Similarly, there are cases where one does not have direct access to any data of the target population and has to resort to publicly available proxy data sampled from a different distribution. In this paper, we present Differentially Private Propensity Scores for Bias Correction (DiPPS), a method for approximating the true data distribution of interest in both of the above settings. We assume that the data analyst has access to a dataset $\tilde{D}$ that was sampled from the distribution of interest in a biased way. As individuals may be more willing to share their data when given a privacy guarantee, we further assume that the analyst is allowed locally differentially private access to a set of samples $D$ from the true, unbiased distribution. Each data point from the private, unbiased dataset $D$ is mapped to a probability distribution over clusters (learned from the biased dataset $\tilde{D}$), from which a single cluster is sampled via the exponential mechanism and shared with the data analyst. This way, the analyst gathers a distribution over clusters, which they use to compute propensity scores for the points in the biased $\tilde{D}$, which are in turn used to reweight the points in $\tilde{D}$ to approximate the true data distribution. It is now possible to compute any function on the resulting reweighted dataset without further access to the private $D$. In experiments on datasets from various domains, we show that DiPPS successfully brings the distribution of the available dataset closer to the distribution of interest in terms of Wasserstein distance. We further show that this results in improved estimates for different statistics.
