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Homomorphic encryption (HE)---the ability to perform computation on encrypteddata---is an attractive remedy to increasing concerns about data privacy indeep learning (DL). However, building DL models that operate on ciphertext iscurrently labor-intensive and requires simultaneous expertise in DL,cryptography, and software engineering. DL frameworks and recent advances ingraph compilers have greatly accelerated the training and deployment of DLmodels to various computing platforms. We introduce nGraph-HE, an extension ofnGraph, Intel's DL graph compiler, which enables deployment of trained modelswith popular frameworks such as TensorFlow while simply treating HE as anotherhardware target. Our graph-compiler approach enables HE-aware optimizations--implemented at compile-time, such as constant folding and HE-SIMD packing, andat run-time, such as special value plaintext bypass. Furthermore, nGraph-HEintegrates with DL frameworks such as TensorFlow, enabling data scientists tobenchmark DL models with minimal overhead.

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