Home   >   CSC-OpenAccess Library   >    Manuscript Information
An Optimal Approach For Knowledge Protection In Structured Frequent Patterns
Cynthia Selvi P, Mohamed Shanavas A.R
Pages - 22 - 29     |    Revised - 01-12-2014     |    Published - 31-12-2014
Volume - 5   Issue - 3    |    Publication Date - December 2014  Table of Contents
Rank Function, Restricted Node, Sanitization, Structured Pattern, Victim States.
Data mining is valuable technology to facilitate the extraction of useful patterns and trends from large volume of data. When these patterns are to be shared in a collaborative environment, they must be protectively shared among the parties concerned in order to preserve the confidentiality of the sensitive data. Sharing of information may be in the form of datasets or in any of the structured patterns like trees, graphs, lattices, etc., This paper propose a sanitization algorithm for protecting sensitive data in a structured frequent pattern(tree).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
B.Yildz, and B.Ergenc, “Hiding Sensitive Predictive Frequent Itemsets”, Proc. of the International MultiConference of Engineers and Computer Scientists 2011, Vol-I.
E.Dasseni, V.S.Verykios, A.K.Elmagarmid & E.Bertino, “Hiding Association Rules by Using Confidence and Support”, Proc. of the 4th Information Hiding Workshop, pages 369– 383, Pittsburg, PA, Apr 2001.
Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran, Fundamentals of Computer Algorithms, Galgotia Pub. Pvt. Ltd, Delhi, 1999.
J.Han, M.Kamber, Data Mining Concepts and Techniches, Oxford University Press , 2009.
J.Pavon, S.Viana, S.Gomez, “Matrix Apriori: speeding up the search for frequent patterns”, Proc. 24th IASTED International Conference on Databases and Applications 2006, pp. 75-82.
M.Atallah, E.Bertino, A.Elmagarmid, M.Ibrahim and V.Verykios “Disclosure Limitation of Sensitive Rules”, Proc. of IEEE Knowledge and Data Engineering Workshop, pages 45–52, Chicago, Illinois, Nov 1999.
P.Cynthia Selvi, A.R.Mohamed Shanavas, “An effective Heuristic Approach for Hiding Sensitive Patterns in Databases”, International Organization of Scientific Research-Journal of Computer Engineering(IOSRJCE) Vol. 5, Issue 1(Sep-Oct 2012), PP 06-11.
P.Cynthia Selvi, A.R.Mohamed Shanavas, “An Improved Item-based Maxcover Algorithm to protect Sensitive Patterns in Large Databases”, International Organization of Scientific Research-Journal of Computer Engineering(IOSRJCE) Vol.14, Issue 4, Oct 2013, Pages 1-5.
P.Cynthia Selvi, A.R.Mohamed Shanavas, “Output Privacy Protection With Pattern-Based Heuristic Algorithm”, International Journal of Computer Science & Information Technology(IJCSIT) Vol 6, No 2, Apr 2014, Pages 141 – 152.
P.Cynthia Selvi, A.R.Mohamed Shanavas, “Towards Information Privacy Using Transaction- Based Maxcover Algorithm”, World Applied Sciences Journal 29 (Data Mining and Soft Computing Techniques): 06-11, 2014.
S.R.M.Oliveira, and O.R.Zaiane, “An Efficient One-Scan Sanitization for Improving the Balance between Privacy and Knowledge Discovery”, Technical Report TR 03-15, Jun 2003.
S.R.M.Oliveira, and O.R.Zaiane, “Privacy preserving Frequent Itemset Mining”, Proc. of the IEEE ICDM Workshop on Privacy, Security, and Data Mining, Pages 43-54, Maebashi City, Japan, Dec 2002.
S.R.M.Oliveira, and O.R.Zaiane, “Secure Association Rule Mining”, Proc. of the 8 th Pacific- Asia Conference on Knowledge Discovery and Data Mining(PAKDD’04), Pages 74-85, Sydney, Australia, May 2004..
The Dataset used in this work for experimental analysis was generated using the generator from IBM Almaden Quest research group and is publicly available from http://fimi.ua.ac.be/data/ .
Y.Saygin, V.S.Verykios, and C.Clifton, “Using Unknowns to Prevent Discovery of Association Rules”, SIGMOD Record, 30(4):45–54, Dec 2001.
Mr. Cynthia Selvi P
Government Collegiate Education, TamilNadu - India
Dr. Mohamed Shanavas A.R
Dept. of Computer Science, Jamal Mohamed College, Trichirappalli 620020, affiliated to Bharathidasan University, Tamilnadu , India - India

View all special issues >>