Online Submission!

Open Journal Systems

An Efficient Technique for Protecting Sensitive Information

Abhishek Diwan, Alpana Singh

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.

Full Text:

PDF

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.

C. Clifton, “Protecting against Data Mining

through Samples,” Proc. 13th IFIP WG11.3 Conf.

Database Security, 1999.

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




DOI: http://dx.doi.org/10.6084/ijact.v2i3.419

Refbacks

  • There are currently no refbacks.