Skip to main content

On Addressing Efficiency Concerns in Privacy-Preserving Mining

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2973)

Abstract

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. To encourage users to provide correct inputs, we recently proposed a data distortion scheme for association rule mining that simultaneously provides both privacy to the user and accuracy in the mining results. However, mining the distorted database can be orders of magnitude more time-consuming as compared to mining the original database. In this paper, we address this issue and demonstrate that by (a) generalizing the distortion process to perform symbol-specific distortion, (b) appropriately chooosing the distortion parameters, and (c) applying a variety of optimizations in the reconstruction process, runtime efficiencies that are well within an order of magnitude of undistorted mining can be achieved.

Keywords

  • Association Rule
  • Frequent Itemsets
  • Association Rule Mining
  • Original Database
  • Privacy Preserve

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-24571-1_9
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-24571-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   179.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, S., Krishnan, V., Haritsa, J.: Providing Efficiency in Privacy-Preserving Mining, Tech. Rep. TR-2003-02, DSL/SERC, Indian Institute of Science (2003), http://dsl.serc.iisc.ernet.in/pub/TR/TR-2003-03.pdf

  2. Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proc. of 20th ACM Symp. on Principles of Database Systems (May 2001)

    Google Scholar 

  3. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of ACM SIGMOD Intl. Conf. on Management of Data (May 1993)

    Google Scholar 

  4. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of 20th Intl. Conf. on Very Large Data Bases (September 1994)

    Google Scholar 

  5. Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proc. of ACM SIGMOD Intl. Conf. on Management of Data (May 2000)

    Google Scholar 

  6. Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure Limitation of Sensitive Rules. In: Proc. of IEEE Knowledge and Data Engineering Exchange Workshop (November 1999)

    Google Scholar 

  7. Dasseni, E., Verykios, V., Elmagarmid, A., Bertino, E.: Hiding Association Rules by Using Confidence and Support. In: Proc. of 4th Intl. Information Hiding Workshop (April 2001)

    Google Scholar 

  8. Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy Preserving Mining of Association Rules. In: Proc. of 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (July 2002)

    Google Scholar 

  9. Evfimievski, A., Gehrke, J., Srikant, R.: Limiting Privacy Breaches in Privacy Preserving Data Mining. In: Proc. of ACM Symp. on Principles of Database Systems (June 2003)

    Google Scholar 

  10. Kantarcioglu, M., Clifton, C.: Privacy-preserving Distributed Mining of Association Rules on Horizontally Partitioned Data. In: Proc. of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (June 2002)

    Google Scholar 

  11. Rizvi, S., Haritsa, J.: Maintaining Data Privacy in Association Rule Mining. In: Proc. of 28th Intl. Conf. on Very Large Databases (August 2002)

    Google Scholar 

  12. Saygin, Y., Verykios, V., Clifton, C.: Using Unknowns to Prevent Discovery of Association Rules. ACM SIGMOD Record 30(4) (2001)

    Google Scholar 

  13. Saygin, Y., Verykios, V., Elmagarmid, A.: Privacy Preserving Association Rule Mining. In: Proc. of 12th Intl. Workshop on Research Issues in Data Engineering (February 2002)

    Google Scholar 

  14. Vaidya, J., Clifton, C.: Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In: Proc. of 8th ACM SIKGDD Intl. Conf. on Knowledge Discovery and Data Mining (July 2002)

    Google Scholar 

  15. Zheng, Z., Kohavi, R., Mason, L.: Real World Performance of Association Rule Algorithms. In: Proc. of 7th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (August 2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Agrawal, S., Krishnan, V., Haritsa, J.R. (2004). On Addressing Efficiency Concerns in Privacy-Preserving Mining. In: Lee, Y., Li, J., Whang, KY., Lee, D. (eds) Database Systems for Advanced Applications. DASFAA 2004. Lecture Notes in Computer Science, vol 2973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24571-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24571-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21047-4

  • Online ISBN: 978-3-540-24571-1

  • eBook Packages: Springer Book Archive