  We present a framework for experimenting with secure multi-party computation
directly in TensorFlow. By doing so we benefit from several properties valuable
to both researchers and practitioners, including tight integration with
ordinary machine learning processes, existing optimizations for distributed
computation in TensorFlow, high-level abstractions for expressing complex
algorithms and protocols, and an expanded set of familiar tooling. We give an
open source implementation of a state-of-the-art protocol and report on
concrete benchmarks using typical models from private machine learning.
