Tuning GaussSieve for Speed

Abstract

AbstractThe area of lattice-based cryptography is growing ever-more prominent as a paradigm for quantum-resistant cryptography. One of the most important hard problem underpinning the security of lattice-based cryptosystems is the shortest vector problem (SVP). At present, two approaches dominate methods for solving instances of this problem in practice: enumeration and sieving. In 2010, Micciancio and Voulgaris presented a heuristic member of the sieving family, known as GaussSieve, demonstrating it to be comparable to enumeration methods in practice. With contemporary lattice-based cryptographic proposals relying largely on the hardness of solving the shortest and closest vector problems in ideal lattices, examining possible improvements to sieving algorithms becomes highly pertinent since, at present, only sieving algorithms have been successfully adapted to solve such instances more efficiently than in the random lattice case. In this paper, we propose a number of heuristic improvements to GaussSieve, which can also be applied to other sieving algorithms for SVP.KeywordsIdeal LatticePopulation CountLattice BasisReference ImplementationOriginal ImplementationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.