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

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