Characterization of differentially private logistic regression

The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy strength and classification accuracy. The proposed approach implements a supervised learning technique and a feature extraction technique to address this challenging problem and generate solutions. The supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. The feature extraction technique transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The proposed approach then employs a signal processing technique called signal-interference-ratio as a measure to quantify the privacy level of the differentially private subspaces; hence, allows data owner learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy.