Title :
A Random Sampling Algorithm for SVP Challenge Based on y-Sparse Representations of Short Lattice Vectors
Author :
Dan Ding ; Guizhen Zhu
Author_Institution :
Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
Abstract :
In this paper, we propose a novel random sampling algorithm for the shortest vector problem (SVP) based on the y-sparse representations of the short lattice vectors. The experimental results show that the random sampling algorithm outperforms the other two SVP algorithms under the benchmarks of SVP challenge[1]. Therefore, the random sampling algorithm is an efficient SVP solver for the shortest vector problem.
Keywords :
cryptography; random processes; sampling methods; vectors; SVP algorithms; SVP solver; lattice-based cryptography; random sampling algorithm; short lattice vectors; shortest vector problem; y-sparse representations; Algorithms; Complexity theory; Cryptography; Lattices; Polynomials; Vectors; Lattice-Based Cryptography; Random Sampling; Shortest Vector Problem (SVP); y-Sparse Representation;
Conference_Titel :
Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4799-7433-7
DOI :
10.1109/CIS.2014.31
