LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions. Differentially private versions of LASSO logistic regression have been developed, but generally produce dense solutions, reducing the intrinsic utility of the LASSO penalty. In this paper, we present a differentially private method for sparse logistic regression that maintains hard zeros. Our key insight is to first train a non-private LASSO logistic regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection. To demonstrate our method's performance, we run experiments on synthetic and real-world datasets.

翻译：LASSO正则化逻辑回归因其内置的特征选择功能特别有用，它允许系数从部署中移除并产生稀疏解。尽管已开发出差分隐私版本的LASSO逻辑回归，但它们通常会产生稠密解，降低了LASSO惩罚的内在效用。本文提出了一种能保持硬零点的差分隐私稀疏逻辑回归方法。我们的关键洞察是：首先训练一个非隐私的LASSO逻辑回归模型，以确定最终模型选择中应使用的非零系数的适当隐私化数量。为验证该方法的性能，我们在合成数据集和真实数据集上进行了实验。