Skip to main content
SpringerLink
Log in

Big Data Security Challenges and Preventive Solutions

  • Conference paper
  • First Online:
Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1042))

Abstract

Big data has opened the possibility of making great advancements in many scientific disciplines and has become a very interesting topic in academic world and in industry. It has also given contributions to innovation, improvements in productivity and competitiveness. However, at present, there are various security risks involved in the process of collection, storage and use. The leakage of privacy caused by big data poses serious problems for the users; also the incorrect or false big data may lead to wrong or invalid analysis of results. The presented work analyzes the technical challenges of implementing big data security and privacy protection, and describes some key solutions to address the issues related with big data security and privacy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Xia, F., Yang, L.T., Wang, L., Vinel, A.: Internet of things. Int. J. Commun. Syst. 25(9), 1101–1102 (2012)

    Article  Google Scholar 

  2. Google search statistics. http://www.internetlivestats.com/google-search-statistics/

  3. Lee, I.: Big data: dimensions, evolution, impacts, and challenges. Bus. Horiz. 60(3), 293–303 (2017)

    Article  Google Scholar 

  4. Nguyen, B., Simkin, L.: The Internet of Things (IoT) and marketing: the state of play, future trends and the implications for marketing. J. Mark. Manage. 33(1–2), 1–6 (2017)

    Article  Google Scholar 

  5. Boyd, D., Crawford, K.: Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf. Commun. Soc. 15(5), 662–679 (2012)

    Article  Google Scholar 

  6. Guo-Jie, L., Xue-Qi, C.: Research status and scientific thinking of big data. Bull. Chin. Acad. Sci. 27(6), 647–657 (2012)

    Google Scholar 

  7. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  8. Arias, M., Arratia, A., Xuriguera, R.: Forecasting with twitter data. ACM Trans. Intell. Syst. Technol. 5(1), 8 (2013)

    Article  Google Scholar 

  9. Arasu, A., Chaudhuri, S., Chen, Z., Ganjam, K.: Experiences with using data cleaning technology for bing services. IEEE Data Eng. Bull. 35(2), 14–23 (2012)

    Google Scholar 

  10. Sarma, A.D., Dong, X.L., Halevy, A.: Data integration with dependent sources. In: Proceedings of the 14th International Conference on Extending Database Technology, ACM, pp. 401–412 (2011)

    Google Scholar 

  11. Elomari, A., Maizate, A., Hassouni, L.: Data storage in big data context: a survey. In: International Conference on Systems of Collaboration (SysCo), pp. 1–4. IEEE (2016)

    Google Scholar 

  12. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  13. Verma, A., Cherkasova, L., Kumar, V.S., Campbell, R.H.: Deadline-based workload management for mapreduce environments: pieces of the performance puzzle. In: Network Operations and Management Symposium (NOMS), pp. 900–905, IEEE (2012)

    Google Scholar 

  14. Dede, E., Fadika, Z., Hartog, J., Govindaraju, M., Ramakrishnan, L., Gunter, D., Canon, R.: Marissa: Mapreduce implementation for streaming science applications. In: IEEE 8th International Conference on E-Science (e-Science), 2012, pp. 1–8, IEEE (2012)

    Google Scholar 

  15. Guo, S., Xiong, J., Wang, W., Lee, R.: Mastiff: a mapreduce-based system for time-based big data analytics. In: IEEE International Conference on Cluster Computing (CLUSTER), 2012, pp. 72–80, IEEE (2012)

    Google Scholar 

  16. Chandramouli, B., Goldstein, J., Duan, S.: Temporal analytics on big data for web advertising. In: IEEE 28th International Conference on Data Engineering, pp. 90–101. IEEE (2012)

    Google Scholar 

  17. Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  18. Wang, Y., Lu, W., Wei, B.: Transactional multi-row access guarantee in the key-value store. In: IEEE International Conference on Cluster Computing (CLUSTER), 2012, pp. 572–575. IEEE (2012)

    Google Scholar 

  19. Hwang, M., Jeong, D.H., Jung, H., Sung, W.K., Shin, J., Kim, P.: A term normalization method for better performance of terminology construction. In: International Conference on Artificial Intelligence and Soft Computing, pp. 682–690. Springer, Berlin (2012)

    Chapter  Google Scholar 

  20. Ketata, I., Mokadem, R., Morvan, F.: Biomedical resource discovery considering semantic heterogeneity in data grid environments. In Integrated Computing Technology, pp. 12–24. Springer, Berlin (2011)

    Google Scholar 

  21. Kang, U., Chau, D.H., Faloutsos, C.: Pegasus: mining billion-scale graphs in the cloud. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5341–5344. IEEE (2012)

    Google Scholar 

  22. Kola, A., More, H., Soderman, S., Gubanov, M.: Generating Unified Famous Objects (UFOs) from the classified object tables. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4771–4773. IEEE (2017)

    Google Scholar 

  23. Tang, J., Liu, J., Zhang, M., Mei, Q.: Visualizing large-scale and high-dimensional data. In: Proceedings of the 25th International Conference on World Wide Web, pp. 287–297. International World Wide Web Conferences Steering Committee (2016)

    Google Scholar 

  24. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)

    Article  Google Scholar 

  25. Meyer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan, England (2013)

    Google Scholar 

  26. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35(2), 137–144 (2015)

    Article  Google Scholar 

  27. Yin, X., Tan, W.: Semi-supervised truth discovery. In: Proceedings of the 20th International Conference on World Wide Web, pp. 217–226. ACM (2011)

    Google Scholar 

  28. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  29. LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM (2005)

    Google Scholar 

  30. Xiao, X., Taom, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 689–700. ACM (2007)

    Google Scholar 

  31. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, 2008. SP 2008, pp. 111–125. IEEE (2008)

    Google Scholar 

  32. Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 739–750. Society for Industrial and Applied Mathematics (2008)

    Google Scholar 

  33. Zhang, L., Zhang, W.: Edge anonymity in social network graphs. In: International Conference on Computational Science and Engineering, 2009. CSE’09, vol. 4, pp. 1–8. IEEE (2009)

    Google Scholar 

  34. Agrawal, R., Haas, P.J., Kiernan, J.: Watermarking relational data: framework, algorithms and analysis. VLDB J. Int. J. Very Large Data Bases 12(2), 157–169 (2013)

    Google Scholar 

  35. Sion, R., Atallah, M., Prabhakar, S.: On watermarking numeric sets. In: International Workshop on Digital Watermarking, pp. 130–146. Springer, Berlin (2002)

    Chapter  Google Scholar 

  36. Guo, F., Wang, J., Li, D.: Fingerprinting relational databases. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 487–492. ACM (2006)

    Google Scholar 

  37. Pease, A., Niles, I., Li, J.: The suggested upper merged ontology: a large ontology for the semantic web and its applications. In: Working Notes of the AAAI-2002 Workshop on Ontologies and the Semantic Web, vol. 28, pp. 7–10 (2002)

    Google Scholar 

  38. Atallah, M.J., Raskin, V., Hempelmann, C.F., Karahan, M., Sion, R., Topkara, U., Triezenberg, K.E.: Natural language watermarking and tamperproofing. In: International Workshop on Information Hiding, pp. 196–212. Springer, Berlin (2002)

    Google Scholar 

  39. Cui, Y., Widom, J., Wiener, J.L.: Tracing the lineage of view data in a warehousing environment. ACM Trans. Database Syst. 25(2), 179–227 (2000)

    Article  Google Scholar 

  40. Muniswamy-Reddy, K.K., Holland, D.A., Braun, U., Seltzer, M.I.: Provenance-aware storage systems. In: USENIX Annual Technical Conference, General Track, pp. 43–56 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University Anoopshahr, Anoopshahr, India

    Nirmal Kumar Gupta

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, India

    Mukesh Kumar Rohil

Authors
  1. Nirmal Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukesh Kumar Rohil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nirmal Kumar Gupta .

Editor information

Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, N.K., Rohil, M.K. (2020). Big Data Security Challenges and Preventive Solutions. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9949-8_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9948-1

  • Online ISBN: 978-981-32-9949-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation