SANITIZING SENSITIVE ASSOCIATION RULES USING FUZZY CORRELATION SCHEME

Authors

  • S. Hameed Department of Computer Science, Mohammad Ali Jinnah University, Islamabad, Pakistan
  • F. Shahzad Department of Computer Science, Mohammad Ali Jinnah University, Islamabad, Pakistan
  • S. Asghar Institute of Information Technology, PMAS University of Arid Agriculture, Rawalpindi, Pakistan

Abstract

Data mining is used to extract useful information hidden in the data. Sometimes this extraction of information leads to revealing sensitive information. Privacy preservation in Data Mining is a process of sanitizing sensitive information. This research focuses on sanitizing sensitive rules discovered in quantitative data. The proposed scheme, Privacy Preserving in Fuzzy Association Rules (PPFAR) is based on fuzzy correlation analysis. In this work, fuzzy set concept is integrated with fuzzy correlation analysis and Apriori algorithm to mark interesting fuzzy association rules. The identified rules are called sensitive. For sanitization, we use modification technique where we substitute maximum value of fuzzy items with zero, which occurs most frequently. Experiments demonstrate that PPFAR method hides sensitive rules with minimum modifications. The technique also maintains the modified data’s quality. The PPFAR scheme has applications in various domains e.g. temperature control, medical analysis, travel time prediction, genetic behavior prediction etc. We have validated the results on medical dataset.

References

S. R. D. M. Oliveira, "Data Transformation for

Privacy-Preserving Data Mining," Doctoral

Dissertation, University of Alberta Edmonton,

Alta (2005).

J. Han and M. Kamber, Data Mining:

Concepts and Techniques, 2nd ed.,The

Morgan Kaufmann Series in Data

Management Systems, Jim Gray, Series

Editor Morgan Kaufmann Publishers, March

ISBN 1-55860-901-6J. Clerk Maxwell,

A Treatise on Electricity and Magnetism, 3rd

Ed., Vol. 2. Oxford: Clarendon (1892) pp.68–

R. Agrawal and R. Srikant, Fast Algorithms

for Mining Association Rules, Presented at

the Proceedings of the 20th VLDB

Conference, Santiago, Chile (1994).

C. Clifton and D. Marks, Security and Privacy

Implications of Data Mining, SIDMOD

Workshop on Data Mining and Knowledge

Discovery (1996).

M. Gupta and R. C. Joshi, International

Journal of Computer Theory and Engineering

, No. 4, (2009) 1793.

Computational Intelligence: Principles,

Techniques and Applications, Prof. Dr Amit

Konar, Springer, The Netherlands, ISBN: 3-

-20898-4 (2005) pages 705.

R. Agrawal and R. Srikant, "Privacy

preserving data mining.," presented at the

Proceedings of the 2000 ACM SIGMOD

International Conference on Management of

Data, New York, NY, USA (2000).

M. Atallah et al., Disclosure Limitation of

Sensitive Rules, Presented at the Knowledge

and Data Engineering Exchange Chicago, IL,

USA (1999).

Y. Saygin et al., Using Unknowns to Prevent

Discovery of Association Rules, Presented at

the ACM SIGMOD Record, New York, USA

(2001).

M. Naeem and S. Asghar, A Novel

Architecture for Hiding Sensitive Association

Rules, Proceedings of the International

Conference on Data Mining, Las Vegas,

Nevada, USA (July 12-15, 2010) CSREA

Press 2010. ISBN: 1-60132-138-4, Robert

Stahlbock and Sven Crone (Eds.)

N. P. Lin and H.-E. Chueh, Fuzzy Correlation

Rules Mining, Presented at the 6th WSEAS

International Conference on Applied

Computer Science, Hangzhou, China (2007).

T. Berberoglu and M. Kaya, Hiding Fuzzy

Association Rules in Quantitative Data,

Presented at the 3rd International

Conference on Grid and Pervasive

Computing Workshops, Kunming, China.

(May 25-28, 2008).

E. Dasseni et al., Hiding Association Rules

by Using Confidence and Support, Presented

at the 4th Information Hiding Workshop,

Pittsburg, PA, USA (2001).

S. R. M. Oliveira and O. R. Zaiane, Privacy

Preserving Frequent Itemset Mining,

Presented at the Workshop on Privacy,

Security and Data Mining, Maebashi City,

Japan (December 2002).

D. Agrawal and C. C. Aggarwal, On the

Design and Quantification of Privacy

Preserving Data Mining Algorithms,

Presented at the Proceedings of the

Twentieth ACM SIGMOD-SIGACT-SIGART

Symposium on Principles of Database

Systems, New York, NY, USA (2001).

S. J. Rizvi and J. R. Haritsa, Maintaining

Data Privacy in Association Rule Mining,

Presented at the 28th International

Conference on Very Large Databases, Hong

Kong, China (2002).

D. Toshniwal and M. Verma, A BorderBased Approach for Hiding Fuzzy Weighted

Sensitve Itemsets, Presented at the

International Conference on DMIN'10, Las

Vegas, Nevada,USA, (2010).

UCI Machine Learning Repository, Breast

Cancer Wisconsin, http://archive.ics.uci.

edu/ml/.

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Published

26-11-2013

How to Cite

[1]
S. Hameed, F. Shahzad, and S. Asghar, “SANITIZING SENSITIVE ASSOCIATION RULES USING FUZZY CORRELATION SCHEME”, The Nucleus, vol. 50, no. 4, pp. 359–367, Nov. 2013.

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Articles