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arXiv:1810.08130cs.CR

Private Machine Learning in TensorFlow using Secure Computation

Morten Dahl, Jason Mancuso, Yann Dupis, Ben Decoste, Morgan Giraud, Ian Livingstone, Justin Patriquin, Gavin Uhma

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Abstract

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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DOI
Subjects
cs.CRCryptography and Security cs.LGMachine Learning

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Nov 2019 to Nov 2019. Found in 1 HN submission from Oct 2018 to Oct 2018. Open submission discussions.

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  1. Machine Learning on Encrypted Data Without Decrypting It 1 comment

    • TF Encrypted has focused more on MPC [1]. At least that's what their arXiv paper [2] talks about. It does seem they are also working to integrate Microsoft SEAL for HE. [1]: https://en.wikipedia.org/wiki/Secure_multi-party_computation [2]: https://arxiv.org/abs/1810.08130

      osaarikiView on HNParent context

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