  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.

Machine Learning;Cryptography and Security