Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling
secure offloading of both storage and computation, and recent innovations in
schemes and implementations have made it all the more attractive. At the same
time, FHE is notoriously hard to use with a very constrained programming model,
a very unusual performance profile, and many cryptographic constraints.
Existing compilers for FHE either target simpler but less efficient FHE schemes
or only support specific domains where they can rely on expert-provided
high-level runtimes to hide complications.
This paper presents a new FHE language called Encrypted Vector Arithmetic
(EVA), which includes an optimizing compiler that generates correct and secure
FHE programs, while hiding all the complexities of the target FHE scheme.
Bolstered by our optimizing compiler, programmers can develop efficient
general-purpose FHE applications directly in EVA. For example, we have
developed image processing applications using EVA, with a very few lines of
code.
EVA is designed to also work as an intermediate representation that can be a
target for compiling higher-level domain-specific languages. To demonstrate
this, we have re-targeted CHET, an existing domain-specific compiler for neural
network inference, onto EVA. Due to the novel optimizations in EVA, its
programs are on average 5.3x faster than those generated by CHET. We believe
that EVA would enable a wider adoption of FHE by making it easier to develop
FHE applications and domain-specific FHE compilers.

本文提出了一种新的全同态加密语言 Encrypted Vector Arithmetic（EVA），并配备了优化编译器，使开发人员可以直接使用 EVA 开发高效的通用 FHE 应用程序，并且可以作为目标编译高级特定领域的语言。此外，由于 EVA 中的新型优化，其程序的速度比 CHET 生成的程序平均快 5.3 倍，EVA 将使更广泛的人更容易地开发 FHE 应用程序和特定领域的 FHE 编译器。