A New Lattice Sieving Algorithm Base on Angular Locality-Sensitive Hashing

  • Ping Wang
  • Dongdong Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10726)


Currently, the space requirement of sieving algorithms to solve the shortest vector problem (SVP) grows as \(2^{0.2075n+o(n)}\), where n is the lattice dimension. In high dimensions, the memory requirement makes them uncompetitive with enumeration algorithms. Shi Bai et al. presents a filtered triple sieving algorithm that breaks the bottleneck with memory \( 2^{0.1887n+o(n)}\) and time \( 2^{0.481n+o(n)}\).

Benefiting from the angular locality-sensitive hashing (LSH) method, our proposed algorithm runs in time \(2^{0.4098n+o(n)}\) with the same space complexity \(2^{0.1887n+o(n)}\) as the filtered triple sieving algorithm. Our experiment demonstrates that the proposed algorithm achieves the desired results. Furthermore, we use the proposed algorithm to solve the closest vector problem (CVP) with the lowest space complexity as far as we know in the literature.


Filtered triple sieving Angular locality-sensitive hashing Shortest vector problem Closest vector problem 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information EngineeringShenzhen UniversityShenzhenChina
  2. 2.College of Computer Science and SoftwareShenzhen UniversityShenzhenChina

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