Evolutionary Techniques in Lattice Sieving Algorithms

Laarhoven, Thijs

doi:10.5220/0007968800310039

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…In 2019, Laarhoven [46] compared sieve algorithms and evolutionary algorithms and explored the possibility to accelerate sieve algorithms with evolutionary techniques.…”

Section: J Other Sieve Algorithmsmentioning

confidence: 99%

“…Inspired by SubSieve technique and progressive sieving, in 2019 Albrecht et al [5] proposed the General Sieve Kernel, which is a stateful machine based on sieve algorithms and solved previously unsolved SVP Challenge instances. Moreover, quantum techniques [41], [49] and evolutionary techniques [46] have been used to improve the efficiency of sieve algorithms in recent years.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Sieve Algorithms in Solving the Shortest Lattice Vector Problem

Sun

Yan

2020

IEEE Access

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As a category of algorithms to solve the shortest lattice vector problem, sieve algorithms have drawn more and more attention due to the prominent performance in recent years. Enumeration algorithms used to perform better in practice even though sieve algorithms are asymptotically faster. Combined with techniques like locality-sensitive hashing and rank reduction, sieve algorithms now are capable of competing with enumeration algorithms. In this work, we study sieve algorithms in solving the shortest vector problem on lattices by categorizing various sieve algorithms and elaborating on ideas and techniques used to improve sieve algorithms. In addition, we present several prospective directions worth future research.

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“…In 2019, Laarhoven [46] compared sieve algorithms and evolutionary algorithms and explored the possibility to accelerate sieve algorithms with evolutionary techniques.…”

Section: J Other Sieve Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Sieve Algorithms in Solving the Shortest Lattice Vector Problem

Sun

Yan

2020

IEEE Access

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“…They also offer high reproducibility because their steps are very simple. Furthermore, Laarhoven [13] investigated the similarities between lattice sieving and evolutionary algorithms (EAs). Since lattice sieving is the most powerful technique for solving the SVP and GAs are a class of EAs, these similarities are of great significance in developing GAs for solving the SVP.…”

Section: Introductionmentioning

confidence: 99%

Improving genetic algorithms for solving the SVP: focusing on low memory consumption and high reproducibility

Fukase

Kaminaga

2022

Iran J Comput Sci

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The shortest vector problem (SVP) is absolutely essential in lattice-based cryptography. In this paper, we significantly improve genetic algorithms (GAs) for solving the SVP. GAs, which are simple and powerful optimization techniques, that have the potential to eliminate the limitations of the existing fast SVP algorithms. We improve the entire phase of the GA construction. Our proposed method is based on the concept of low memory consumption and high reproducibility, and this is the main and significant difference between our algorithm and the other SVP algorithms. Our contributions are twofold. First, we developed a new GA for solving the SVP and achieved a considerable improvement in the running time performance. Second, we interpreted certain genetic operations, such as mutation and crossover, in the context of lattices, which has not been done in previous studies. The general result of this paper is that we showed the potential of GAs in the field of lattices.

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“…Plus, other classes of SVP-solvers, such as enumeration and sieving, have been subject of intense and ongoing investigation and optimization through the past decade (e.g. [7,13,2,12]). But Voronoi cell-based SVP-solvers, to the best of our knowledge, have not been optimized since their first publication [1], back in 2002.…”

Section: Introductionmentioning

confidence: 99%

Optimized Voronoi-Based Algorithms for Parallel Shortest Vector Computation

Mariano¹,

Cabeleira²,

Santos³

et al. 2022

Cybersecurity and High-Performance Computing Environments

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Voronoi-cell-based algorithm by Micciancio et al. 1.2.2 Relevant vectors by Agrell et al. 1.3 EXPERIMENTAL SETUP 1.4 ALGORITHM ANALYSIS 1.4.1 Correlation between the norm of target vectors and solution vectors 1.4.2 Percentage of target vectors that generate the shortest vector 1.5 ALGORITHMIC OPTIMIZATIONS 1.5.1 Pruned decoding 1.5.1.1 Simple pruning. 1.5.1.2 Gaussian pruning. 1.5.1.3 Combined pruning. 1.5.2 Increasing norm sort 1.6 PARALLEL IMPLEMENTATIONS FOR CPUS AND GPUS 1.6.1 CPU 1.6.1.1 Original version (no pruning and no presorting). 1.6.1.2 Pruned version without sorting. 1.6.1.3 Pruned version with sorting.

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Evolutionary Techniques in Lattice Sieving Algorithms

Cited by 6 publications

References 33 publications

A Review of Sieve Algorithms in Solving the Shortest Lattice Vector Problem

A Review of Sieve Algorithms in Solving the Shortest Lattice Vector Problem

Improving genetic algorithms for solving the SVP: focusing on low memory consumption and high reproducibility

Optimized Voronoi-Based Algorithms for Parallel Shortest Vector Computation

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