论文标题
Coinpress：实用的私人平均值和协方差估计
CoinPress: Practical Private Mean and Covariance Estimation
论文作者
论文摘要
我们提出了简单的差异私有估计器，以实现多元次级数据数据的平均值和协方差，这些数据在小样本量下是准确的。我们在理论上和经验上使用合成和现实世界数据集证明了我们的算法的有效性 - 表明它们的渐近错误率与最新的理论界限相匹配，并且它们的表现优于所有先前的方法。具体而言，先前的估计器要么在小样本量下的经验准确性较弱，要么在多元数据方面表现较差，要么要求用户为参数提供强大的先验估计值。
We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of our algorithms both theoretically and empirically using synthetic and real-world datasets -- showing that their asymptotic error rates match the state-of-the-art theoretical bounds, and that they concretely outperform all previous methods. Specifically, previous estimators either have weak empirical accuracy at small sample sizes, perform poorly for multivariate data, or require the user to provide strong a priori estimates for the parameters.
