8373

Accelerating Fully Homomorphic Encryption on GPUs

Wei Wang,Yin Hu,Lianmu Chen,Xinming Huang, Berk Sunar
Department of Electrial and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
IEEE High Performance Extreme Computing Conference(HPEC ’12), 2012
@article{wang2012accelerating,

   title={Accelerating Fully Homomorphic Encryption on GPUs},

   author={Wang, W. and Hu, Y. and Chen, L. and Huang, X. and Sunar, B.},

   year={2012}

}

Download Download (PDF)   View View   Source Source   

747

views

In a major breakthrough, in 2009 Gentry introduced the first plausible construction of a fully homomorphic encryption (FHE) scheme. FHE allows the evaluation of arbitrary functions directly on encrypted data on untwisted servers. In 2010, Gentry and Halevi presented the first FHE implementation on an IBM x3500 server. However, this implementation remains impractical due to the high latency of encryption and recryption. The GentryHalevi (GH) FHE primitives, utilize multi-million-bit modular multiplications and additions – time-consuming tasks for general purpose processors. In the GH-FHE implementation, the most computationintensive arithmetic operation is modular multiplication. In this paper, the million-bit multiplication is calculated in two steps: large-number multiplication and modular reduction. Strassen’s FFT based algorithm is used so that Graphics processing units (GPU) can employ massive parallelism to accelerate the largenumber number multiplication. In what follows, the Barrett Modular Reduction algorithm is used to realize modular multiplication. We implemented the encryption, decryption and recryption primitives on the NVIDIA C2050. Experimental results show factors of up to 7.68, 7.4 and 6.59 speed improvement for encryption, decryption and recrypt, respectively, when compared to the GH implementation for the small setting in dimension 2048.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

149 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1238 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: