• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Polynomial Multiplication in NTRU Prime: Comparison of Optimization Strategies on Cortex-M4
 
  • Details
  • Full
Options
2020
Journal Article
Title

Polynomial Multiplication in NTRU Prime: Comparison of Optimization Strategies on Cortex-M4

Abstract
This paper proposes two different methods to perform NTT-based polynomial multiplication in polynomial rings that do not naturally support such a multiplication. We demonstrate these methods on the NTRU Prime key-encapsulation mechanism (KEM) proposed by Bernstein, Chuengsatiansup, Lange, and Vredendaal, which uses a polynomial ring that is, by design, not amenable to use with NTT. One of our approaches is using Good's trick and focuses on speed and supporting more than one parameter set with a single implementation. The other approach is using a mixed radix NTT and focuses on the use of smaller multipliers and less memory. On a ARM Cortex-M4 microcontroller, we show that our three NTT-based implementations, one based on Good's trick and two mixed radix NTTs, provide between 32% and 17% faster polynomial multiplication. For the parameter-set ntrulpr761, this results in between 16% and 9% faster total operations (sum of key generation, encapsulation, and decapsulation) and requires between 15% and 39% less memory than the current state-of-the-art NTRU Prime implementation on this platform, which is using Toom-Cook-based polynomial multiplication.
Author(s)
Alkim, Erdem
Cheng, Dean Yun-Li
Chung, Chi-Ming Marvin
Evkan, Hülya  
Huang, Leo Wei-Lun
Hwang, Vincent
Li, Ching-Lin Trista
Niederhagen, Ruben
Shih, Cheng-Jhih
Wälde, Julian  
Yang, Bo-Yin
Journal
IACR transactions on cryptographic hardware and embedded systems  
Open Access
DOI
10.46586/tches.v2021.i1.217-238
Language
English
Fraunhofer-Institut für Sichere Informationstechnologie SIT  
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024