Skip to main content
SpringerLink
Log in

A Survey on Risks of Big Data Privacy

  • Conference paper
  • First Online:
International Conference on Applications and Techniques in Cyber Security and Intelligence (ATCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 580))

  • 1201 Accesses

Abstract

With the rapid development and wide application of big data technology, a huge amount of data is gathered into big data platform, not only from a wide variety, but also with rapid growth speed. While improving social economic and making social benefits, big data technology is facing great risks and challenges in the aspect of big data security and privacy. Currently, big data privacy has become an urgent problem in the era of big data application which attracts a large number of reports and concerns, and its importance and urgency can’t be ignored. This paper first describes the characteristics and categories of big data privacy, then analysis privacy risks during the whole life cycle of big data processing in deep, including data collection, data integration and fusion, data analysis and data sharing, etc. Finally, this paper discusses the goals and solutions on how to control and prevent big data privacy risks.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Weitzner, D.J., Bruce, E.J.: Big data privacy workshop: advancing the state of the art in technology and practice. http://web.mit.edu/bigdata-pri/index.html. 3 Mar 2014

  2. Holdren, J.P., Lander, E.S.: Big data privacy: a technological perspective [R/OL]. http://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_privacy_-_may_2014.pdf. 1 May 2014

  3. Xiaofeng, M., Xiang, C.: Big data management: concepts, techniques and challenges. J. Comput. Res. Dev. 50(1), 146–169 (2013)

    Google Scholar 

  4. Alina, E., Sungjin, I., Moseley, B.: Fast clustering using MapReduce. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), pp. 681–689. ACM, New York (2011)

    Google Scholar 

  5. Caetano, T.J., Traina, A.J.M., Lopez, J., et al.: Clustering very large multi-dimensional datasets with MapReduce. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), pp. 690–698. ACM, New York (2011)

    Google Scholar 

  6. Chierichetti, F., Dalvi, N., Kumar, R.: Correlation clustering in MapReduce. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), pp. 641–650. ACM, New York (2014)

    Google Scholar 

  7. Hsieh, C.J., Chang, K.W., Lin, C.J., et al. A dual coordinate descent method for large-scale linear SVM. In: Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp. 408–415. AAAI, Menlo Park, CA (2008)

    Google Scholar 

  8. Schmidt, M., Roux, N.L., Bach, F.: Convergence rates of inexact proximal-gradient methods for convex optimization. In: Processing Systems (NIPS 2011), pp. 1458–1466. Springer, Berlin (2011)

    Google Scholar 

  9. Quick, D., Choo, K.-K.R.: Big forensic data management in heterogeneous distributed systems: quick analysis of multimedia forensic data. In: Software: Practice and Experience (2017). doi:10.1002/spe.2429

  10. Quick, D., Choo, K.-K.R.: Digital forensic intelligence: data subsets and open source intelligence (DFINT + OSINT): a timely and cohesive mix. In: Future Generation Computer Systems (2017). doi:10.1016/j.future.2016.12.032

  11. Quick, D., Choo, K.-K.R.: Pervasive social networking forensics: intelligence and evidence from mobile device extracts. J. Netw. Comput. Appl. 86, 24–33 (2017)

    Article  Google Scholar 

  12. Quick, D., Choo, K.-K.R.: Big forensic data reduction: digital forensic images and electronic evidence. Clust. Comput. 19(2), 723–740 (2016)

    Article  Google Scholar 

  13. Quick, D., Choo, K.-K.R.: Data reduction and data mining framework for digital forensic evidence: storage, intelligence, review, and archive. Trends Issues Crime Crim. Justice 480, 1–11 (2014)

    Google Scholar 

  14. Quick, D., Choo, K.-K.R.: Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit. Investig. 11(4), 273–294 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

The authors of this paper are members of Shanghai Engineering Research Center of Intelligent Video Surveillance. Our research was sponsored by following projects: the National Natural Science Foundation of China (61403084, 61402116); Program of Science and Technology-Commission of Shanghai Municipality (Nos. 15530701300, 15XD15202000); 2012 IoT Program of Ministry of Industry and Information Technology of China; Key Project of the Ministry of Public Security (No. 2014JSYJA007); the Project of the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University (ESSCKF 2015-03); Shanghai Rising-Star Program (17QB1401000).

Author information

Authors and Affiliations

  1. The Third Research Institute of the Ministry of Public Security, 339 Bisheng Road, Shanghai, China

    Kui Wang

Authors
  1. Kui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kui Wang .

Editor information

Editors and Affiliations

  1. Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, Victoria, Australia

    Jemal Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

  3. School of Computing and Mathematics, Charles Sturt University, Albury, New South Wales, Australia

    Rafiqul Islam

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Wang, K. (2018). A Survey on Risks of Big Data Privacy. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67071-3_23

  • Published:

  • Publisher Name: Edizioni della Normale, Cham

  • Print ISBN: 978-3-319-67070-6

  • Online ISBN: 978-3-319-67071-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation