FPGA implementation of modular multiplier in residue number system

Yinan Kong, Md Selim Hossain

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

2 Citations (Scopus)

Abstract

This work presents a description of a highperformance hardware implementation of a Montgomery modular multiplier using a residue number system (RNS). An RNS can be considered as self-defense against simple power analysis (SPA) and differential power analysis (DPA) attacks, and can be used for public-key cryptography, such as the Rivest, Shamir and Adleman (RSA) cryptosystem and elliptic curve cryptosystems (ECC). Various kinds of security are required for Big Data analysis. The proposed RNS-based modular multiplier is suitable for public-key cryptography that can be used for Big Data security. It is implemented on field-programmable gate-array (FPGA) technology and optimized by trying different variants of the Montgomery Algorithm on it. The proposed RNS-based modular multiplication takes only 22 ns on the Xilinx Virtex-II FPGA. In addition, it needs relatively few resources on the FPGA, needing only 68 slices.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationThe 2018 IEEE International Conference on Internet of Things and Intelligence
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages137-140
Number of pages4
ISBN (Electronic)9781538673584, 9781538673577
ISBN (Print)9781538673591
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018 - Bali, Indonesia
Duration: 1 Nov 20183 Nov 2018

Conference

Conference2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018
CountryIndonesia
CityBali
Period1/11/183/11/18

Keywords

  • Montgomery modular multiplier
  • residue number system (RNS)
  • field-programmable gate array (FPGA)
  • high performance
  • public-key cryptography
  • Big Data

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