Fast Arithmetic Hardware Library for RLWE-Based Homomorphic Encryption

Rashmi Agrawal, Lake Bu, Michel A. Kinsy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

With billions of devices connected over the internet, the rise of sensor-based electronic devices have led to cloud computing being used as a commodity technology service. These sensor-based devices are often small and limited by power, storage, or compute capabilities, and hence, they achieve these capabilities via cloud services. However, this gives rise to data privacy issues as sensitive data is stored and computed over the cloud, which at most times, is a shared resource. Homomorphic encryption can be used along with cloud services to perform computations on encrypted data, guaranteeing data privacy. While about a decade's work on improving homomorphic encryption has ensured its practicality, it is still several magnitudes slower than expected, making it expensive and infeasible to use. In this work, we propose a first-of-its-kind FPGA-based arithmetic hardware library that focuses on accelerating the key arithmetic operations involved in Ring Learning with Error (RLWE) based homomorphic encryption. We design and implement the FPGAbased Residue Number System (RNS), Chinese Remainder Theorem (CRT), modulo inverse and modulo reduction operations as a first step. For all of these operations, we include a hardware cost efficient serial, and a fast parallel implementation in the library. A modular and parameterized design approach helps in easy customization, provides flexibility to extend these operations for use in most homomorphic encryption applications, and fits well into emerging FPGA-equipped cloud architectures.

Original languageEnglish (US)
Title of host publicationProceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages206
Number of pages1
ISBN (Electronic)9781728158037
DOIs
StatePublished - May 2020
Externally publishedYes
Event28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020 - Fayetteville, United States
Duration: May 3 2020May 6 2020

Publication series

NameProceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020

Conference

Conference28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020
Country/TerritoryUnited States
CityFayetteville
Period5/3/205/6/20

ASJC Scopus subject areas

  • Computational Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing

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