Peer-to-Peer Networking and Applications

, Volume 11, Issue 2, pp 252–264 | Cite as

Protecting lightweight block cipher implementation in mobile big data computing

A GPU-based approach
  • Weidong Qiu
  • Bozhong Liu
  • Can Ge
  • Lingzhi Xu
  • Xiaoming Tang
  • Guozhen Liu
Article
  • 96 Downloads

Abstract

The Mobile Big Data Computing is a new evolution of computing technology in data communication and processing. The data generated from mobile devices can be used for optimization and personalization of mobile services and other profitable businesses. Mobile devices are usually with limited computing resources, thus the security measures are constrained. To solve this problem, lightweight block ciphers are usually adopted. However, due to the easily exposed environment, lightweight block ciphers are apt to suffer from differential power attack. To counteract this attack, Nikova et al. proposed a provably secure method, namely sharing, to protect the cipher’s implementation. But the complexity of sharing method is so high, making this method not practical. To address this issue, in this paper, we propose a GPU-based approach of sharing a 4-bit S-box by automatic search. GPU is a promising acceleration hardware with powerful parallel computing. By analyzing the sharing method carefully, we devise an optimal approach, namely OptImp, that improves the performance massively. The experiment results show that the proposed approach can achieve up to 300 times faster than the original method. With our approach, the sharing method can be used to protect lightweight block ciphers in practice.

Keywords

Mobile big data Lightweight block cipher Threshold implementation GPU optimization 

Notes

Acknowledgments

This work is sponsored by program of Shanghai Technology Research Leader under Grant No. 16XD1424400, program of Key Technologies Research and Development under Grant No. 2014BAK06B02, and program for New Century Excellent Talents in University under Grant No. NCET-12-0358.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Weidong Qiu
    • 1
  • Bozhong Liu
    • 1
  • Can Ge
    • 1
  • Lingzhi Xu
    • 1
  • Xiaoming Tang
    • 1
  • Guozhen Liu
    • 1
  1. 1.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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