nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data

10/23/2018
by   Fabian Boemer, et al.
0

Homomorphic encryption (HE)--the ability to perform computations on encrypted data--is an attractive remedy to increasing concerns about data privacy in the field of machine learning. However, building models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in deep learning, cryptography, and software engineering. Deep learning frameworks, together with recent advances in graph compilers, have greatly accelerated the training and deployment of deep learning models to a variety of computing platforms. Here, we introduce nGraph-HE, an extension of the nGraph deep learning compiler, which allows data scientists to deploy trained models with popular frameworks like TensorFlow, MXNet and PyTorch directly, while simply treating HE as another hardware target. This combination of frameworks and graph compilers greatly simplifies the development of privacy-preserving machine learning systems, provides a clean abstraction barrier between deep learning and HE, allows HE libraries to exploit HE-specific graph optimizations, and comes at a low cost in runtime overhead versus native HE operations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/24/2018

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

The Deep Learning (DL) community sees many novel topologies published ea...
research
08/12/2019

nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data

In previous work, Boemer et al. introduced nGraph-HE, an extension to th...
research
11/08/2017

DLVM: A modern compiler infrastructure for deep learning systems

Deep learning software demands reliability and performance. However, man...
research
01/29/2022

Flashlight: Enabling Innovation in Tools for Machine Learning

As the computational requirements for machine learning systems and the s...
research
01/27/2019

Moving Deep Learning into Web Browser: How Far Can We Go?

Recently, several JavaScript-based deep learning frameworks have emerged...
research
04/10/2023

Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM

In the past few years, more and more AI applications have been applied t...
research
10/01/2018

CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes...

Please sign up or login with your details

Forgot password? Click here to reset