Data mining is the process of extracting
interesting patterns that are hidden in raw data. One of the
major tasks of finding the interesting patterns from the
large repositories of data is performed by applying the
association rules of different kinds. While carrying out this
process, some sensitive information face disclosure in front
of the unauthorized users which should be taken care of
and this is carried out by the process of privacy
preservation. This paper presents an approach that
modifies few transactions in the transaction database so
that it gains the support factor sensitive rules and
confidence factor of sensitive rules and reduces side effects.
The technique presented in this paper will increase the
number of hidden sensitive rules and also will help in
reducing the number of modified entries.
Published In : IJCAT Journal Volume 2, Issue 4
Date of Publication : April 2015
Pages : 121 - 124
Figures :--
Tables : 01
Publication Link :Association of Data with Privacy Preserving of
Sensitive Information
Nikhil N. Vaidya : received the B.E. degree in
computer science from the Rajeev Gandhi
College of Engineering, Chandrapur, Nagpur
University, India. Currently doing M.TECH. in
C.S.E. in GH Raisoni academy of
Engineering and Technology, Nagpur
University Nagpur, India. His research
interest includes network security, forensic
communication Neural Networks and fuzzy logic, and Data
Mining.
Amit Pimpalkar : is Currently working as
Associate Professor in GH Raisoni
Academy of Engineering and Technology,
Nagpur University Nagpur, India. His
research interest includes Network
Security, Theory of computation,compiler
design and Image processing.
Ashwini Meshram : is currently working
as Assistant Professor in GH Raisoni
Academy of Engineering and Technology,
Nagpur University Nagpur, India. His
research interest includes Network
Security,compiler design and Data Mining.
Data Mining
Association Rules
Privacy Preserving Data Mining
Sensitive Items
Association Rule
Hiding
With all the methods that are presented in this paper,
modification of the transaction is going to be carried out
in such a way that, the two things that is the count of
hidden sensitive rules and the modified entries are taken
under considerations. In maximum cases, there is no
generation of false rules while carrying out the
procedure of hiding rules.
[1] Razali, A.M. and S. Ali, “Generating treatment plan
in medicine: A data mining approach”. In the
Proceedings of Am. J. Applied Sci., 6 2009: pp. 345-
351.
[2] Oliveira, S., &Zaiane, O. “Privacy preserving
frequent itemset mining” In the Proceedings of IEEE
International Conference On Data Mining,November
2002,pp. 43–54.
[3] Saygin, Y., V.S. Verykios and A.K. Elmagarmid.
“Privacy preserving association rule mining”. In the
Proceedings of the 12th International Workshop on
Research Issues in Data Engineering: Engineering ECommerce/
E-Business Systems, Feb. 24-25, IEEE
Xplore Press, SanJose, CA. USA.2002, pp. 151-158.
[4] Vaidya, J., H. Yu and X. Jiang. “Privacy preserving
SVM classification”.In the Proceedings of Knowl.
Inform.2008 Syst., pp. 161-178.
[5] Verykios, V.S., A.K. Elmagarmid, E. Bertino, Y.
Saygin and E. Dasseni. “Association rule hiding”. In
the Proceedings of IEEE Trans. Knowl. Data
Eng.,2004 16:pp.434-447.
[6] Evfimievski, A., R. Srikant, R. Agrawal and J.
Gehrke. “Privacy preserving mining of association
rules”. Proceedings of the 8th ACMSIGKDD
International Conference on Knowledge Discovery
and Data Mining, July 23-25, ACM Press,
Edmonton, AB., Canada,2002 pp. 1-12.
[7] Wang, S.L., B. Parikh and A. Jafari. ” Hiding
informative association rule sets”. Exp. Syst. Appli.,
33: pp. 316-323, 2007.
[8] Y. H. Wu, C.M. Chiang and A.L.P. Chen. “Hiding
Sensitive Association Rules with Limited Side
Effects”, In the Proceedings of IEEE Transactions on
Knowledge and Data Engineering, vol.19 (1), pp.
29–42, Jan. 2007.
[9] V.S. Verykios, A.K. Elmagarmid, E. Bertino, Y.
Saygin, and E. DasseniIn the Proceedings of
“Association rule hiding, IEEE Transactions on
Knowledge and Data engineering”, vol. 16(4), pp.
434–447, April 2004. [10] K. Duraiswamy, and D. Manjula, “Advanced
Approach in Sensitive Rule Hiding”, In the
Proceedings of Modern Applied Science, vol. 3(2),
Feb. 2009.
[11] X. Sun and P.S. Yu, “A Border-Based Approach for
Hiding Sensitive Frequent Itemsets”, In Proc. Fifth
IEEE Int’l Conf. Data Mining (ICDM ’05), pp. 426–
433, Nov. 2005.
[12] Y. Saygin, V. S. Verykios, and A. K. Elmagarmid,
“Privacy preserving association rule mining”, In
Proc. Int’l Workshop on ResearchIssues in Data
Engineering (RIDE 2002), pp. 151–163, 2002.
[13] T. Mielikainen, “On inverse frequent set mining,” In
Proc. 3rd IEEE ICDM Workshop on Privacy
Preserving Data Mining. IEEE Computer Society,
pp.18–23, 2003.
[14] J. Vaidya and C. Clifton, “Privacy preserving
association rule mining in vertically partitioned
data,” In proc. Int’l Conf. Knowledge Discovery and
Data Mining, pp. 639–644, July 2002.
[15] R. Agrawal and R. Srikant, “ Privacy preserving data
mining “,In the Proceedings of ACM SIGMOD
Conference on Management of Data pages 439-
450,Dallas, Texas, May 2000.
[16] Yogendra Kumar Jain et al. / International Journal on
Computer Science and Engineering (IJCSE) ISSN