Name
Abstract | ||
|---|---|---|
In this paper, we present a GPU-based parallel algorithm for the Learning With Errors (LWE) problem using a lattice-based Bounded Distance Decoding (BDD) approach. To the best of our knowledge, this is the first GPU-based implementation for the LWE problem. Compared to the sequential BDD implementation of Lindner-Peikert and pruned-enumeration strategies by Kirshanova [1], our GPU-based implementation is almost faster by a factor 6 and 9 respectively. The used GPU is NVIDIA GeForce GTX 1060 6G. We also provided a parallel implementation using two GPUs. The results showed that our algorithm is scalable and faster than the sequential version (Lindner-Peikert and pruned-enumeration) by a factor of almost 13 and 16 respectively. Moreover, the results showed that our parallel implementation using two GPUs is more efficient than Kirshanova et al.'s parallel implementation using 20 CPU-cores. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2019.2961091 | IEEE ACCESS |
Keywords | DocType | Volume |
Learning with error,lattice-based cryptography,LLL algorithm,shortest vector problem,closest vector problem,bounded distance decoding,GPU,cryptanalysis | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed S. Esseissah | 1 | 0 | 0.34 |
| Ashraf Bhery | 2 | 10 | 1.99 |
| Hatem M. Bahig | 3 | 23 | 7.53 |