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.