Improved Heuristics for Short Linear Programs

In this article, we propose new heuristics for minimising the amount of XOR gates required to compute a system of linear equations in GF(2). We first revisit the well known Boyar-Peralta strategy and argue that a proper randomisation process during the selection phases can lead to great improvements...

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Bibliographic Details
Main Authors: Quan Quan Tan, Thomas Peyrin
Format: Article
Language:English
Published: Ruhr-Universität Bochum 2019-11-01
Series:Transactions on Cryptographic Hardware and Embedded Systems
Subjects:
Online Access:https://tches.iacr.org/index.php/TCHES/article/view/8398
Description
Summary:In this article, we propose new heuristics for minimising the amount of XOR gates required to compute a system of linear equations in GF(2). We first revisit the well known Boyar-Peralta strategy and argue that a proper randomisation process during the selection phases can lead to great improvements. We then propose new selection criteria and explain their rationale. Our new methods outperform state-of-the-art algorithms such as Paar or Boyar-Peralta (or open synthesis tools such as Yosys) when tested on random matrices with various densities. They can be applied to matrices of reasonable sizes (up to about 32 × 32). Notably, we provide a new implementation record for the matrix underlying the MixColumns function of the AES block cipher, requiring only 94 XORs.
ISSN:2569-2925