PRECISE INFORMATION HIDING TECHNIQUES
Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
Download this PDF format
This research paper contains an overview of the new and rapidly emerging research area of privacy preserving data mining. Privacy preserving in data mining has been a heart favorite topic of researchers from many years. Every organization contains sensitive data & such data is needed to be protected from the unauthorized access. This paper contains the comprehensive survey of traditional and modern privacy preserving data mining methods. Advantages and disadvantages of the existing algorithms are discussed a classification hierarchy that sets the basis for analyzing the work which has been performed in this context.
 Aris Gkoulalas–Divanis;Vassilios S. Verykios ?Association Rule Hiding For Data Mining? Springer, DOI 10.1007/978-1-4419-6569-1, Springer Science + Business Media, LLC 2010
 M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V. S. Verykios ?Disclosure limitation of sensitive rules, ?.In Proc. of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX’99), pp. 45–52, 1999.
 Vassilios S. Verykios, A.K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni, ?Association Rule Hiding,? IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 4, pp. 434-447, 2004.
 Shyue-Liang Wang; Bhavesh Parikh,; Ayat Jafari, ?Hiding informative association rule sets?, ELSEVIER, Expert Systems with Applications 33 (2007) 316–323,2006
 Shyue-LiangWang ;Dipen Patel ;Ayat Jafari ;Tzung-Pei Hong, ?Hiding collaborative recommendation association rules?, Published online: 30 January 2007, Springer Science+Business Media, LLC 2007
 Shyue-Liang Wang; Rajeev Maskey; Ayat Jafari; Tzung-Pei Hong ? Efficient sanitization of informative association rules? ACM , Expert Systems with Applications: An International Journal, Volume 35, Issue 1-2, July, 2008
 Chih-Chia Weng; Shan-Tai Chen; Hung-Che Lo, ?A Novel Algorithm for Completely Hiding Sensitive Association Rules?, IEEE Intelligent Systems Design and Applications, 2008.,vol 3, pp.202-208, 2008
 Modi, C.N.; Rao, U.P.; Patel, D.R., ?Maintaining privacy and data quality in privacy preserving association rule mining?, IEEE 2008 Seventh International Conference on Machine Learning and Applications, pp 1-6, 2010
 Stanley R. M. Oliveira; Osmar R. Za¨_ane, ?Privacy Preserving Frequent Itemset Mining?, IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, Maebashi City, Japan. Conferences in Research and Practice in Information Technology, Vol. 14.2002
 Y.Saygin, V. S. Verykios, and C. Clifton, ?Using Unknowns to Prevent Discovery of Association Rules,? ACM SIGMOD, vol.30(4), pp. 45–54, Dec. 2001.
 Y. Saygin, V. S. Verykios, and A. K. Elmagarmid, ?Privacy preserving association rule mining,? In Proc. Int’l Workshop on Research Issues in Data Engineering (RIDE 2002), 2002,pp. 151–163.
 E. Pontikakis, Y. Theodoridis, A. Tsitsonis, L. Chang, and V. S. Verykios. A quantitative and qualitative analysis of blocking in association rule hiding. In Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society (WPES), pages 29–30, 2004.
 H. Mannila and H. Toivonen, ?Levelwise search and borders of theories in knowledge discovery,? Data Mining and Knowledge Discovery, vol.1(3), pp. 241–258, Sep. 1997.
 X. Sun and P. S. Yu. A border–based approach for hiding sensitive frequent itemsets. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pages 426– 433, 2005.
 X. Sun and P. S. Yu. Hiding sensitive frequent item sets by a border–based approach. Computing science and engineering, 1(1):74–94, 2007.
 G. V. Moustakides and V. S. Verykios. A max–min approach for hiding frequent itemsets. In Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), pages 502–506, 2006.
 G. V. Moustakides and V. S. Verykios. A maxmin approach for hiding frequent item sets. Data and Knowledge Engineering, 65(1):75–89, 2008.
 A. Gkoulalas-Divanis and V.S. Verykios, ?An Integer Programming Approach for Frequent Item set Hiding,? In Proc. ACM Conf. Information and Knowledge Management (CIKM ’06), Nov. 2006.
 A. Gkoulalas-Divanis and V.S. Verykios, ?Exact Knowledge Hiding through Database Extension,? IEEE Transactions on Knowledge and Data Engineering, vol. 21(5), pp. 699–713, May 2009.
 Ila Chandrakar, Manasa, Usha Rani, and Renuka. Hybrid Algorithm for Association Rule mining. Journal of Computer Science 6(12), pages 1494-1498, 2010
 Belwal, Varsheney, Khan, Sharma, Bhattacharya. Hiding sensitive association rules efficiently by introducing new variable hiding counter. Pages 130-134, 978-2008, IEEE.
 Shikha Sharma, An Extended Method for Privacy Preserving Association Rule Mining, in International Journal of Advanced Research of Computer Science & Software Engineering, volume2 issue 10, October 2012.
PPDM, Support, Confidence, Information security.