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Private Machine Learning in TensorFlow using Secure Computation
lib:42f43944769eb57c (v1.0.0)
Authors: Morten Dahl,Jason Mancuso,Yann Dupis,Ben Decoste,Morgan Giraud,Ian Livingstone,Justin Patriquin,Gavin Uhma
ArXiv: 1810.08130
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1810.08130v2
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.
ArXiv: 1810.08130
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1810.08130v2
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.
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