Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Database Systems pp 2147–2150Cite as

Privacy-Preserving Data Mining

  • Reference work entry

  • 320 Accesses

Definition

Data Mining techniques that use specialized approaches to protect against the disclosure of private information may involve anonymizing private data, distorting sensitive values, encrypting data, or other means to ensure that sensitive data is protected.

Historical Background

The field of privacy-preserving data mining began in 2000 with two papers of that name[1,4]. Both papers addressed construction of decision trees, approximating the ID3 algorithm while limiting disclosure of data. While the problems appeared similar on the surface, the fundamental difference in privacy constraints shows the complexity of this field. In [1], the assumption was that individuals were providing their own data to a common server, and added noise to sensitive values to protect privacy. The key to the technique was to discover the original distribution of the data, enabling successful construction of the decision tree. In [4], the data was presumed to be divided between two (or a small...

This is a preview of subscription content, 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   2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Agrawal R. and Srikant R. Privacy-preserving data mining. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 439–450

    Google Scholar 

  2. Atallah M.J., Elmongui H.G., Deshpande V., and Schwarz L.B., Secure supply-chain protocols. In Proc. IEEE Int. Conf. on E-Commerce, 2003, pp. 293–302.

    Google Scholar 

  3. Kaski S. Dimensionality reduction by random mapping. In Proc. Int. Joint Conference on Neural Networks, 1999, pp. 413–418.

    Google Scholar 

  4. Lindell Y. and Pinkas B. Privacy preserving data mining. In Advances in Cryptology – CRYPTO 2000. Springer, 2000, pp. 36–54.

    Google Scholar 

  5. Oliveira S.R.M. and Zaïane O.R. Privacy preserving clustering by data transformation. In Proc. 18th Brazilian Symp. on Databases, 2003.

    Google Scholar 

  6. Vaidya J. and Clifton C. Privacy-preserving outlier detection. In Proc. 2004 IEEE Int. Conf. on Data Mining, 2004, pp. 233–240.

    Google Scholar 

  7. Vaidya J., Clifton C., and Zhu M. Privacy Preserving Data Mining. ser. Springer, Berlin, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Chris Clifton

Authors
  1. Chris Clifton
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Clifton, C. (2009). Privacy-Preserving Data Mining. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_270

Download citation

Publish with us

Policies and ethics

Search

Navigation