This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.
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Inputs
Output
Distributed Learning Without Distress.pdf
Makefile
README.md
adult_data.p
fixed.c
fixed.h
kddcup98_data_70k.p
kddcup99_data_70k.p
model.py
modelAggregate.c
modelAggregate.oc
modelAggregate.oh
model_wrapper.py
ofixed.oc
ofixed.oh
ofixed_constants.h
ofixed_constants.h.template
util.c
util.h

README.md

distributedMachineLearning

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.