An Efficient Technique for Protecting Sensitive Information

Authors

  • Diwan A M Tech Student, Medicaps Institute of Technology & Management Indore (M.P.)
  • Singh A Asst. Professor, Medicaps Institute of Technology & Management Indore (M.P.)

Keywords:

Data mining, Data hiding, Support, Confidence, Association rules

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. Two important terms support and confidence are associated with each of the association rule. Actually any rule is called as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes we do not want to disclose sensitive rules to the public because of confidentiality purposes. There are many approaches to hide certain association rules which take the support and confidence as a base for algorithms and many more). The proposed work has the basis of reduction of support and confidence of sensitive rules but this work is not editing or disturbing the given database of transactions directly .The proposed algorithm uses some modified definition of support and confidence so that it would hide any desired sensitive association rule without any side effect. Actually the enhanced technique is using the same method (as previously used method) of getting association rules but modified definitions of support and confidence are used.

References

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.

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.

R. Agrawal and R. Srikant, "Privacy preserving data mining", In ACM SIGMOD Conference on Management of Data, pages 439450, Dallas, Texas, May 2000.

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 200

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.

C. Clifton and D. Marks, “Security and Privacy Implications of Data Mining,” Proc. 1996 ACM Workshop Data Mining and Knowledge

Discovery, 1996.

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Published

2024-02-26

How to Cite

Diwan, A., & Singh, A. (2024). An Efficient Technique for Protecting Sensitive Information. COMPUSOFT: An International Journal of Advanced Computer Technology, 2(03), 79–82. Retrieved from https://ijact.in/index.php/j/article/view/16

Issue

Section

Original Research Article

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