# CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

## TL;DR

CodedPrivateML is a scalable framework that enables privacy-preserving distributed machine learning by ensuring data and model privacy while maintaining high training efficiency and convergence guarantees.

## Contribution

It introduces CodedPrivateML, a novel approach combining information-theoretic privacy with parallel training, outperforming cryptographic methods in speed.

## Key findings

- Achieves privacy threshold with theoretical guarantees
- Demonstrates convergence for logistic and linear regression
- Provides significant speedup over MPC-based approaches

## Abstract

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via extensive experiments on Amazon EC2, we demonstrate that CodedPrivateML provides significant speedup over cryptographic approaches based on multi-party computing (MPC).