Data scientists often seek to identify the most important features in high-dimensional datasets. This can be done through $L_1$-regularized regression, but this can become inefficient for very high-dimensional datasets. Additionally, high-dimensional regression can leak information about individual datapoints in a dataset. In this paper, we empirically evaluate the established baseline method for feature selection with differential privacy, the two-stage selection technique, and show that it is not stable under sparsity. This makes it perform poorly on real-world datasets, so we consider a different approach to private feature selection. We employ a correlations-based order statistic to choose important features from a dataset and privatize them to ensure that the results do not leak information about individual datapoints. We find that our method significantly outperforms the established baseline for private feature selection on many datasets.

本研究针对高维数据集中特征选择的有效性问题进行了探索，且现有的两阶段选择方法在稀疏情况下表现不佳。作者提出了一种基于相关性的排序统计方法来选择重要特征，确保结果的隐私性。实验证明，该方法在多种数据集上显著优于传统基线方法，具有良好的应用潜力。

差分隐私相关性下的特征选择