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PLDI 2020
Mon 15 - Fri 19 June 2020
Fri 19 Jun 2020 14:40 - 15:00 at PLDI Research Papers live stream - Language Design II Chair(s): Mike Dodds

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 $5.3\times$ 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.

Fri 19 Jun

Displayed time zone: Pacific Time (US & Canada) change

14:20 - 15:40
HipHop.js: (A)Synchronous Reactive Web Programming
PLDI Research Papers
Gérard Berry Collège de France, France, Manuel Serrano Inria, France
EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
PLDI Research Papers
Roshan Dathathri University of Texas at Austin, USA, Blagovesta Kostova EPFL, Switzerland, Olli Saarikivi Microsoft Research, Redmond, Wei Dai Microsoft Research, n.n., Kim Laine Microsoft Research, Redmond, Madan Musuvathi Microsoft Research
Towards an API for the Real Numbers
PLDI Research Papers
Responsive Parallelism with Futures and State
PLDI Research Papers
Stefan K. Muller Carnegie Mellon University, USA, Kyle Singer Washington University in St. Louis, USA, Noah Goldstein Washington University in St. Louis, USA, Umut A. Acar Carnegie Mellon University, USA, Kunal Agrawal Washington University in St. Louis, USA, I-Ting Angelina Lee Washington University in St. Louis, USA