This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning. Based on the paper "Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization" (link -- To be added) that has been accepted at NIPS 2018.
The code contains privacy preserving implementation of L2 Regularized Logistic Regression and Linear Regression models.