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CHET: an optimizing compiler for fully-homomorphic neural-network inferencing

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Todd MytkowiczAuthors Info & Claims

PLDI 2019: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 142 - 156

https://doi.org/10.1145/3314221.3314628

Published: 08 June 2019 Publication History

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Abstract

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. Recent cryptographic advances have pushed FHE into the realm of practical applications. However, programming these applications remains a huge challenge, as it requires cryptographic domain expertise to ensure correctness, security, and performance.

CHET is a domain-specific optimizing compiler designed to make the task of programming FHE applications easier. Motivated by the need to perform neural network inference on encrypted medical and financial data, CHET supports a domain-specific language for specifying tensor circuits. It automates many of the laborious and error prone tasks of encoding such circuits homomorphically, including encryption parameter selection to guarantee security and accuracy of the computation, determining efficient tensor layouts, and performing scheme-specific optimizations.

Our evaluation on a collection of popular neural networks shows that CHET generates homomorphic circuits that outperform expert-tuned circuits and makes it easy to switch across different encryption schemes. We demonstrate its scalability by evaluating it on a version of SqueezeNet, which to the best of our knowledge, is the deepest neural network to be evaluated homomorphically.

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[1]

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 265-283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi.

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