Speeding Up Homomorpic Hashing Using GPUs

Homomorphic hash functions (HHFs) have been applied into peer-to-peer networks with erasure coding or network coding to defend against pollution attacks. Unfortunately HHFs are computationally expensive for contemporary CPUs, This paper to exploit the computing power of graphic processing units (GPU...

Full description

Saved in:
Bibliographic Details
Published inIEEE International Conference on Communications (2003) pp. 1 - 5
Main Authors Kaiyong Zhao, Xiaowen Chu, Mea Wang, Yixin Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Application software
Arithmetic
Central Processing Unit
Computer architecture
Computer graphics
Cryptography
Libraries
Network coding
Peer to peer computing
Pollution
Online AccessGet full text
ISSN1550-3607
DOI10.1109/ICC.2009.5199483

Cover

More Information
Summary:Homomorphic hash functions (HHFs) have been applied into peer-to-peer networks with erasure coding or network coding to defend against pollution attacks. Unfortunately HHFs are computationally expensive for contemporary CPUs, This paper to exploit the computing power of graphic processing units (GPUs) for homomorphic hashing. Specifically, we demonstrate how to use NVIDIA GPUs and the computer unified device architecture (CUDA) programming model to achieve 38 times of speedup over the CPU counterpart. We also develop a multi-precision modular arithmetic library on CUDA platform, which is not only key to our specific application, but also very useful for a large number of cryptographic applications.
ISSN:1550-3607
DOI:10.1109/ICC.2009.5199483