Mon 24 Jun 2019 10:00 - 10:20 at 224AB - Language Design II Chair(s): Santosh Nagarakatte

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

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.

Mon 24 Jun

Displayed time zone: Tijuana, Baja California change

10:00 - 11:00
Language Design IIPLDI Research Papers at 224AB
Chair(s): Santosh Nagarakatte Rutgers University, USA
CHET: An Optimizing Compiler for Fully-Homomorphic Neural-Network Inferencing
PLDI Research Papers
Roshan Dathathri University of Texas at Austin, USA, Olli Saarikivi , Hao Chen Microsoft Research, Kim Laine Microsoft Research, n.n., Kristin Lauter Microsoft Research, n.n., Saeed Maleki Microsoft Research, Madan Musuvathi Microsoft Research, Todd Mytkowicz Microsoft Research
DOI Pre-print Media Attached
Usuba: High-Throughput and Constant-Time Ciphers, by Construction
PLDI Research Papers
Darius Mercadier Sorbonne Universités —UPMC Univ Paris 06, Pierre-Evariste Dagand LIP6/CNRS
Media Attached
FaCT: A DSL for Timing-Sensitive Computation
PLDI Research Papers
Sunjay Cauligi University of California, San Diego, Gary Soeller , Brian Johannesmeyer University of California at San Diego, USA, Fraser Brown Stanford University, Riad S. Wahby Stanford University, USA, John Renner University of California, San Diego, Benjamin Gregoire INRIA, Gilles Barthe IMDEA Software Institute, Ranjit Jhala University of California, San Diego, Deian Stefan University of California San Diego
Media Attached