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


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.


Mobile big data Lightweight block cipher Threshold implementation GPU optimization 



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.


  1. 1.
    Akkar M, Giraud C (2001) An implementation of DES and aes, secure against some attacks. In: Cryptographic hardware and embedded systems - CHES 2001. Springer, Generators, Paris, pp 309–318Google Scholar
  2. 2.
    Alemneh E (2010) Share nonlinear gates in the presence of glitches. In: Master thesis of the University of Twente. NetherlandsGoogle Scholar
  3. 3.
    Blömer J, Guajardo J, Krummel V (2004) Provably secure masking of AES. In: Selected areas in cryptography - SAC 2004, pp 69–83Google Scholar
  4. 4.
    Bogdanov A, Knudsen LR, Leander G, Paar C, Poschmann A, Robshaw MJB, Seurin Y, Vikkelsoe C (2007) PRESENT: an ultra-lightweight block cipher. In: Cryptographic hardware and embedded systems - CHES 2007, pp 450–466Google Scholar
  5. 5.
    Buja AG, Latip SFA (2015) The direction of lightweight ciphers in mobile big data computing. Procedia Comput Sci 72 :469–476CrossRefGoogle Scholar
  6. 6.
    Fan Z, Qiu F, Kaufman AE, Yoakum-Stover S (2004) GPU cluster for high performance computing. In: Proceedings of the ACM/IEEE SC2004 conference on high performance networking and computing, p 47Google Scholar
  7. 7.
    Gong Z, Nikova S, Law YW (2011) KLEIN: A new family of lightweight block ciphers. In: RFID. Security and privacy - RFIDSec 2011. Springer, Amherst, pp 1–18Google Scholar
  8. 8.
    Ishai Y, Sahai A, Wagner D (2003) Private circuits: securing hardware against probing attacks. In: CRYPTO 2003. Springer, Santa Barbara, pp 463–481Google Scholar
  9. 9.
    Jiang H, Fujishiro M, Kodera H, Yanagisawa M, Togawa N (2015) Scan-based side-channel attack on the camellia block cipher using scan signatures. IEICE Trans 98-A(12):2547–2555CrossRefGoogle Scholar
  10. 10.
    Kocher PC, Jaffe J, Jun B (1999) Differential power analysis. In: Advances in cryptology - CRYPTO ’99, pp 388–397Google Scholar
  11. 11.
    Liu B, Gong Z, Qiu W (2016) Automatic search of threshold implementations of 4-bit s-boxes resisting dpa. will be published in Chinese Journal of ElectronicsGoogle Scholar
  12. 12.
    Mangard S, Popp T, Gammel BM (2005a) Side-channel leakage of masked CMOS gates. In: CT-RSA 2005. Springer, San Francisco, pp 351–365Google Scholar
  13. 13.
    Mangard S, Pramstaller N, Oswald E (2005b) Successfully attacking masked AES hardware implementations. In: Cryptographic hardware and embedded systems - CHES 2005. Springer, Edinburgh, pp 157–171Google Scholar
  14. 14.
    Moon S, Yoon C (2015) Information retrieval system using the keyword concept net of the P2P service-based in the mobile cloud environment. Peer-to-Peer Netw Appl 8(4):596–609CrossRefGoogle Scholar
  15. 15.
    Nikova S, Rijmen V, Schläffer M (2011) Secure hardware implementation of nonlinear functions in the presence of glitches. J Cryptol 24(2):292–321MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Popp T, Mangard S (2005) Masked dual-rail pre-charge logic: Dpa-resistance without routing constraints. In: Cryptographic hardware and embedded systems - CHES 2005. Springer, pp 172–186Google Scholar
  17. 17.
    Poschmann A, Moradi A, Khoo K, Lim C, Wang H, Ling S (2011) Side-channel resistant crypto for less than 2, 300 GE. J Cryptol 24(2):322–345MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Prouff E (2005) DPA attacks and s-boxes. In: Fast software encryption - FSE 2005, pp 424–441Google Scholar
  19. 19.
    Rabaey JM (1996) Digital integrated circuits: a design perspective. Prentice-Hall Inc., Upper Saddle RiverGoogle Scholar
  20. 20.
    Shanmugam D, Selvam R, Annadurai S (2014) Differential power analysis attack on SIMON and LED block ciphers. In: Security, privacy, and applied cryptography engineering - SPACE 2014, pp 110–125Google Scholar
  21. 21.
    Shibutani K, Isobe T, Hiwatari H, Mitsuda A, Akishita T, Shirai T (2011) Piccolo: an ultra-lightweight blockcipher. In: Cryptographic hardware and embedded systems - CHES 2011, pp 342– 357Google Scholar
  22. 22.
    Smart NP (2000) Physical side-channel attacks on cryptographic systems. Softw Focus 1(2):6–13CrossRefGoogle Scholar
  23. 23.
    Waluyo AB, Srinivasan B, Taniar D (2005) Research in mobile database query optimization and processing. Mob Inf Syst 1(4):225–252Google Scholar
  24. 24.
    Waluyo AB, Taniar D, Srinivasan B (2013) The convergence of big data and mobile computing. In: International conference on network-based information systems - NBiS 2013, pp 79– 84Google Scholar
  25. 25.
    Xu J, Lee W, Tang X, Gao Q, Li S (2006) An error-resilient and tunable distributed indexing scheme for wireless data broadcast. IEEE Trans Knowl Data Eng 18(2):392–404Google Scholar

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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