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A Parallel AES Encryption Algorithms and Its Application

  • Jingang ShiEmail author
  • Shoujin Wang
  • Limei Sun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)

Abstract

With the rapid development of the Internet technology, data security is becoming more and more important. Data encryption is an important means to protect data security. AES is an important algorithm for encrypting data. However, when the amount of data needed to be encrypted is large, the traditional AES algorithm runs very slowly. This paper presents a parallel AES encryption algorithm based on MapReduce architecture, which can be applied in large-scale cluster environment. It can improve the efficiency of massive data encryption and decryption by parallelization. And the paper designs a parallel cipher block chaining mode to apply AES algorithm. Experiments show that the proposed algorithm has good scalability and efficient performance, and can be applied to the security of massive data in cloud computing environment.

Keywords

AES algorithm MapReduce Cloud computing 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61702345).

References

  1. 1.
    Fernandes, D.A.B., Soares, L.F.B., Gomes, J.V., et al.: Security issues in cloud environments: a survey. Int. J. Inf. Secur. 13(2), 113–170 (2014)CrossRefGoogle Scholar
  2. 2.
    Ahuja, S.P., Komathukattil, D.: A survey of the state of cloud security. Netw. Commun. Technol. 1(2), 66–75 (2012)Google Scholar
  3. 3.
    Wu, W.L., Feng, D.G.: The State-of-the-art of research on block cipher mode of operation. Chin. J. Comput. 29(1), 21–36 (2006)MathSciNetGoogle Scholar
  4. 4.
    Qing, S.H.: Construction of parallel cryptographic systems. J. Softw. 11(10), 1286–1293 (2000)Google Scholar
  5. 5.
    Yin, X.C., Chen, W.H., Xie, L.: Parallel processing model of the block cipher. J. Chin. Comput. Syst. 26(4), 600–603 (2005)Google Scholar
  6. 6.
    Yang, J., Ge, W., Cao, P., et al.: An area-efficient design of reconfigurable s-box for parallel implementation of block ciphers. IEICE Electron. Express 13(11), 1–9 (2016)Google Scholar
  7. 7.
    Lee, W.K., Cheong, H.S., Phan Raphael, C.W., et al.: Fast implementation of block ciphers and PRNGs in maxwell GPU architecture. Cluster Comput. 19(1), 335–347 (2016)CrossRefGoogle Scholar
  8. 8.
    Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53, 72–77 (2010)CrossRefGoogle Scholar
  9. 9.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  10. 10.
    White, T.: Hadoop the Definitive Guide. O’Reilly, USA (2009)Google Scholar
  11. 11.
    Landset, S., Khoshgoftaar, T.M., Richter, A.N., et al.: A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data 2(1), 1–36 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and Control EngineeringShenyang Jianzhu UniversityShenyangChina

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