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Reseach Article

Privacy-Preserving Data Sharing Using Data Reconstruction Based Approach

Published on March 2012 by Kshitij Pathak, Narendra S. Chaudhari, Aruna Tiwari
Communication Security
Foundation of Computer Science USA
COMNETCS - Number 1
March 2012
Authors: Kshitij Pathak, Narendra S. Chaudhari, Aruna Tiwari
4529cd04-7747-4b51-8fd5-0c5998386732

Kshitij Pathak, Narendra S. Chaudhari, Aruna Tiwari . Privacy-Preserving Data Sharing Using Data Reconstruction Based Approach. Communication Security. COMNETCS, 1 (March 2012), 64-68.

@article{
author = { Kshitij Pathak, Narendra S. Chaudhari, Aruna Tiwari },
title = { Privacy-Preserving Data Sharing Using Data Reconstruction Based Approach },
journal = { Communication Security },
issue_date = { March 2012 },
volume = { COMNETCS },
number = { 1 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 64-68 },
numpages = 5,
url = { /specialissues/comnetcs/number1/5485-1013/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Communication Security
%A Kshitij Pathak
%A Narendra S. Chaudhari
%A Aruna Tiwari
%T Privacy-Preserving Data Sharing Using Data Reconstruction Based Approach
%J Communication Security
%@ 0975-8887
%V COMNETCS
%N 1
%P 64-68
%D 2012
%I International Journal of Computer Applications
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 preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. One known fact which is very important in data mining is discovering the association rules from database of transactions where each transaction consists of set of items. There are many approaches to hide certain association rules which take the support and confidence as a base for algorithms ([1, 2, 6] and many more). This research work discusses privacy and security issues that are likely to affect data mining projects. This research work focuses on further investigating reconstruction-based techniques for association rule hiding, the problem of sharing sensitive knowledge by sanitization and hope that proposed solution will fetch up the new reconstruction-based research track and work well according to the evaluation metrics including hiding effects, data utility, and time performance

References
  1. 1. Shyue-Liang Wang, Yu-Huei Lee, Steven Billis, Ayat Jafari "Hiding Sensitive Items in Privacy Preserving Association Rule Mining" 2004 IEEE International Conference on Systems, Man and Cybernetics
  2. Vassilios S. Verykios, Ahmed K. Elmagarmid, Elisa Bertino, Yucel Saygin and Elena Dasseni"Association Rule Hiding", IEEE Transactions on Knowledge and Data Engineering, Vol. 16No. 4, April 2004.
  3. Yucel Saygin, Vassilios S. Verykios, Chris Clifton "Using Unknowns to Prevent Discovery of Association Rule" SIGMOD Record, Vol. 30, No.4, December 2001.
  4. Chris Clifton, Don Marks "Security and Privacy Implications of Data Mining", In Proceedings of the 1996 ACM SIGMOD Workshop on Data Mining and Knowledge Discovery.
  5. R. Agrawal and R. Srikant, "Privacy preserving data mining", In ACM SIGMOD Conference on Management of Data, pages 439450, Dallas, Texas, May 2000.
  6. Vi-Hung Wu, Chia-Ming Chiang, and Arbee L.P. Chen, Senior Member, IEEE Computer Society Hiding Sensitive Association Rules with Limited Side Effects IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 19, NO.1, JANUARY 2007
  7. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases", In Proceedings of ACM SIGMOD International Conference on Management of Data Washington DC, May 1993.
  8. S. Oliveira, o. Zaiane, "Algorithms for Balancing Privacy and Knowledge Discovery in Association Rule Mining", Proceedings of 71 th International Database Engineering and Applications SYmposium (IDEAS03), Hong Kong, July 2003.
  9. Wu, Y.H., Chiang, C.M., and Chen, A.L.P. Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering, 2007,19(1):29-42.
  10. Fienberg, S. and Slavkovic, A. Preserving the confidentiality of categorical statistical data bases when releasing information for association rules. Data Mining and Knowledge Discovery, 11(2):155-180,2005.
  11. State-of-the-art in Privacy Preserving Data Mining Vassilios S. Verykios, Elisa Bertino, Igor Nai Fovino Loredana Parasiliti Provenza, Yucel Saygin, Yannis Theodoridisl SIGMOD Record, Vol. 33, No.1, March 2004, Pages: 50 - 57.
  12. C. Clifton and D. Marks, “Security and Privacy Implications of Data Mining,” Proc. 1996 ACM Workshop Data Mining and Knowledge Discovery, 1996.
  13. C. Clifton, “Protecting against Data Mining through Samples,” Proc. 13th IFIP WG11.3 Conf. Database Security, 1999.
  14. T. Johnsten and V.V. Raghavan, “Impact of Decision-Region Based Classification Mining Algorithms on Database Security,” Proc. 13th IFIP WG11.3 Conf. Database Security, 1999.
  15. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V. Verykios, “Disclosure Limitation Of Sensitive Rules,” Proc. Knowledge and Data Exchange Workshop, 1999.
  16. Chen, X., Orlowska, M., and Li, X. A new framework for privacy preserving data sharing. In: Proc. of the 4th IEEE ICDM Workshop: Privacy and Security Aspects of Data Mining. IEEE Computer Society, 2004. 47-56.
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Item sets Data Mining Cursors Association Rules