Advertisement

Big Data Security on Cloud Servers Using Data Fragmentation Technique and NoSQL Database

  • Nelson SantosEmail author
  • Giovanni L. Masala
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)

Abstract

Cloud computing has become so popular that most sensitive data are hosted on the cloud. This fast-growing paradigm has brought along many problems, including the security and integrity of the data, where users rely entirely on the providers to secure their data. This paper investigates the use of the pattern fragmentation to split data into chunks before storing it in the cloud, by comparing the performance on two different cloud providers. In addition, it proposes a novel approach combining a pattern fragmentation technique with a NoSQL database, to organize and manage the chunks. Our research has indicated that there is a trade-off on the performance when using a database. Any slight difference on a big data environment is always important, however, this cost is compensated by having the data organized and managed. The use of random pattern fragmentation has great potential, as it adds a layer of protection on the data without using as much resources, contrary to using encryption.

Keywords

Cloud security Data fragmentation NoSQL database Big data 

References

  1. 1.
    NIST, Definition of Cloud Computing. National Institute of Standards and Technology (2011)Google Scholar
  2. 2.
    Cloud Security Alliance: Top threats to cloud computing. Version 1.0 (2010)Google Scholar
  3. 3.
    Bahrami, M., Singhal, M.: The role of cloud computing architecture in Big Data. In: Pedrycz, W., Chen, S.-M. (eds.) Information Granularity, Big Data, and Computational Intelligence. SBD, vol. 8, pp. 275–295. Springer, Cham (2015)Google Scholar
  4. 4.
    Kumar, P., Raj, H., Jelciana, P.: Exploring data security issues and solutions in cloud computing. Procedia Comput. Sci. 125, 691–697 (2018)CrossRefGoogle Scholar
  5. 5.
    Hegarty, R., Haggerty, J.: Extrusion detection of illegal files in cloud-based systems. Int. J. Space Based Situ. Comput. 5(3), 150–158 (2015)CrossRefGoogle Scholar
  6. 6.
    Dev, H., Sen, T., Basak, M., Ali, M.: An approach to protect the privacy of cloud data from data mining based attacks. In: Companion: High Performance Computing, Networking Storage and Analysis, pp. 1006–1115. IEEE, Salt Lake City (2012)Google Scholar
  7. 7.
    Chakraborty, D., Sarkar, P.: A new mode of encryption providing a tweakable strong pseudo-random permutation. In: Robshaw, M. (ed.) FSE 2006. LNCS, vol. 4047, pp. 293–309. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Gharajedaghi, J.: Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture. Elsevier, Boston (2011)CrossRefGoogle Scholar
  9. 9.
    Bahramim, M., Singhal, M.: A light-weight permutation based method for data privacy in Mobile Cloud Computing. In: 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 189–198. IEEE, San Francisco (2015)Google Scholar
  10. 10.
    Bahrami, M., Singhal, M.: CloudPDB: a light-weight data privacy schema for cloud-based databases. In: 2016 International Conference on Computing, Networking and Communications (ICNC), pp. 1–5, Kauai (2016)Google Scholar
  11. 11.
    Kapusta, K., Memmi, G.: Data protection by means of fragmentation in distributed storage systems. In: 2015 International Conference on Protocol Engineering (ICPE) and International Conference on New Technologies of Distributed Systems (NTDS), pp. 1–8. IEEE, Paris (2015)Google Scholar
  12. 12.
    Lentini, S., Grosso, E., Masala, G.: A comparison of data fragmentation techniques in cloud servers. In: Proceedings of International Conference on Emerging Internet, Data and Web Technologies (EIDWT 2018), Tirana (2018)CrossRefGoogle Scholar
  13. 13.
    Rafique, A., Van Landuyt, D., Reniers, V., Joosen, W.: Leveraging NoSQL for scalable and dynamic data encryption in multi-tenant SaaS. In: 2017 IEEE Trustcom/BigDataSE/ICESS, pp. 885–892, Sydney (2017)Google Scholar
  14. 14.
    Alsirhani, A., Bodorik, P., Sampalli, S.: Improving database security in cloud computing by fragmentation of data. In: International Conference on Computer and Applications, pp. 43–49. IEEE, Dubai (2017)Google Scholar
  15. 15.
    Masala, G.L., Ruiu, P., Grosso, E.: Biometric authentication and data security in cloud computing. In: Daimi, K. (eds.) Computer and Network Security Essentials. Springer, Heidelberg (2018)Google Scholar
  16. 16.
    MongoDB Homepage. https://www.mongodb.com/. Accessed 3 Mar 2018
  17. 17.
    CouchDB Homepage. http://couchdb.apache.org/. Accessed 3 Mar 2018
  18. 18.
    SANS Institute: Extending your business network through a virtual private network (VPN). SANS Infosec Reading room (2016)Google Scholar
  19. 19.
    AWS Amazon Homepage. https://amazon.com. Accessed 2 Mar 2018
  20. 20.
    Microsoft Azure Homepage. https://azure.microsoft.com/en-gb/. Accessed 2 Mar 2018
  21. 21.
    Federal Information. Announcing the Advanced Encryption Standard (AES). Federal Information Processing Standards Publication 197 (2001)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Big Data Group, School of Computing, Electronics and MathematicsPlymouth UniversityPlymouthUK

Personalised recommendations