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.
翻译:在调查中,通常由个人决定他们是否愿意参与,这会导致参与偏差:愿意分享其数据的个人可能不会代表整个人口。同样,在有些案例中,人们无法直接查阅目标人口的任何数据,而不得不使用从不同分布中抽样的公开替代数据。在本文中,我们展示了不同私家私人Propentisity 评分(DipPS),这是一种接近上述两种设置中利益的真正数据分布的方法。我们假设数据分析员可以访问从利益分布中抽样的 $\ tilde{D} 美元。同样,当个人在获得隐私保证时,可能更愿意分享他们的数据。我们进一步展示的是,从私家私家私家私家私家的纯值 $Dset setections $@D} 数据流出一个数据集 $Drequald dreald dreald dreal 。在使用单组数据流中,从一个单组化数据流数据流到直径数据流数据流到直径分析机的美元。