Distributed Privacy-Preserving Empirical Risk Minimization
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" (http://papers.nips.cc/paper/7871-distributed-learning-without-distress-privacy-preserving-empirical-risk-minimization) that has been accepted at NIPS 2018.
The code contains privacy preserving implementation of L2 Regularized Logistic Regression and Linear Regression models.
Execute make files in
model_aggregate_laplace directories using
make command to obtain the respective
a.out executable files.