Design and Implementation of Data-at-Rest Encryption for Hadoop

  • Authors

    • Siti Hanisah Kamaruzaman
    • Wan Nor Shuhadah Wan Nik
    • Mohamad Afendee Mohamed
    • Zarina Mohamad
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.15.11212
  • Encryption, Hadoop, Map-Reduce, Cloud Computing.

  • Abstract

    The manuscript should contain an abstract. The security aspects in Cloud computing is paramount in order to ensure high quality of Service Level Agreement (SLA) to the cloud computing customers. This issue is more apparent when very large amount of data is involved in this emerging computing environment. Hadoop is an open source software framework that supports large data sets storage and processing in a distributed computing environment and well-known implementation of Map Reduce. Map Reduce is one common programming model to process and handle a large amount of data, specifically in big data analysis. Further, Hadoop Distributed File System (HDFS) is a distributed, scalable and portable file system that is written in java for Hadoop framework. However, the main problem is that the data at rest is not secure where intruders can steal or converts the data stored in this computing environment. Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. It is shown that the implementation of AES encryption algorithm is capable to secure data stored in HDFS to some extent.   

     

     

  • References

    1. [1] https://www.researchgate.net/publication/301887194_Efficient_Hybrid_MAES_Encryption_Algorithm_for_Mobile_Device_Data_Security_at_Rest_in_Cloud_Environment.

      [2] Yang, C., Lin, W., & Liu, M. (2013, September). A novel triple encryption scheme for Hadoop-based cloud data security. In Emerging Intelligent Data and Web Technologies (EIDWT), 2013 Fourth International Conference on (pp. 437-442). IEEE.

      [3] Sharma, P. P., & Navdeti, C. P. (2014). Securing big data Hadoop: a review of security issues, threats, and solution. Int. J. Comput. Sci. Inf. Technol, 5.

      [4] https://securosis.com/assets/library/reports/Securing_Hadoop_Final_V2.pdf

      [5] Padmavathi, B., & Kumari, S. R. (2013). A survey on performance analysis of DES, AES and RSA algorithm along with LSB substitution. Int. J. Sci. Res, 2(4), 170-174.

      [6] Pol, U. R. (2016). Big Data Analysis :Comparision of Hadoop MapReduce and Apache Spark Big Data Analysis : Comparision of Hadoop MapReduce and Apache. International Journal of Engineering Science and Computing, 6(6), 6389–6391. https://doi.org/10.4010/2016.1535

      [7] Microsoft [Online] Available: https://msdn.microsoft.com/enus/library/windows/desktop/aa381939(v=vs.85).asp

      [8] Cohen, J., & Acharya, S. (2013, December). Towards a trusted hadoop storage platform: Design considerations of an aes based encryption scheme with tpm rooted key protections. In Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC) (pp. 444-451). IEEE.

      [9] Bende, S., & Shedge, R. (2016). Dealing with Small Files Problem in Hadoop Distributed File System. Procedia Computer Science, 79, 1001-1012.

      [10] Cohen, J. C., & Acharya, S. (2014). Towards a trusted HDFS storage platform: Mitigating threats to Hadoop infrastructures using hardware-accelerated encryption with TPM-rooted key protection. Journal of Information Security and Applications, 19(3), 224-244.

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  • How to Cite

    Hanisah Kamaruzaman, S., Nor Shuhadah Wan Nik, W., Afendee Mohamed, M., & Mohamad, Z. (2018). Design and Implementation of Data-at-Rest Encryption for Hadoop. International Journal of Engineering & Technology, 7(2.15), 54-57. https://doi.org/10.14419/ijet.v7i2.15.11212

    Received date: 2018-04-06

    Accepted date: 2018-04-06

    Published date: 2018-04-06