  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).
