A Survey: Preventing Discovering Association Rules For Large Data Base

Mohnish Patel¹ , Aasif Hasan² , Sushil Kumar³

Section:Survey Paper, Product Type: Isroset-Journal
Vol.1 , Issue.3 , pp.35-38, May-2013

Online published on Jul 07, 2013

Copyright © Mohnish Patel, Aasif Hasan , Sushil Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Data products are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Whether data is personal or corporate data, data mining offers the potential to reveal what other regard as sensitive (private). In some cases, it may be of mutual benefit for two parties (even competitors) to share their data for an analysis task. Sensitive knowledge which can be mined from a database by using data mining algorithms should also be excluded, because such knowledge can equally well compromise data privacy, as we will indicate. The main objective in privacy preserving data mining is to develop algorithms for modifying the original data in some way, so that the private data and private knowledge remain private even after the mining process. The problem that arises when confidential information can be derived from released data by unauthorized users is also commonly called the “database inference” problem.

Key-Words / Index Term :
Association Rule Mining, Data Mining

References :
[1] Alexandre Evfimievski and Tyrone Grandison, “Privacy Preserving Data Mining” at IBM Almaden Research Center, 2007.
[2] Tzung-Pei Hong, Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan, ”Evolutionary privacy-preserving data mining”, 19-23 Sept. 2010.
[3] Adam, N. R. & Wortmann, J. C., “Security-Control Methods for Statistical Databases: A Comparative Study”, ACM Computing Surveys, Vol. 21, N. 4, pp. 515–556, 1989.
[4] ‘Shyue-Lia Wang; Yu-Huei Lee; Billis, S.; Jafari, A. Systems, Man and Cybernetics “Hiding Sensitive Items In Privacy Preserving Association Rule Mining”, 2004 IEEE International Conference on Volume 4, Issue , 10-13 Page(s): 3239 – 3244, Oct. 2004.
[5] Cornelia Gyorodi Robert Gyorodii, prof. Stefan Holban, “A Comparative Study of Association Rules Mining Algorithms",. Jan 2004.
[6] Qi Wang and Krishnamoorthy Siva Kumar, USA:”On the Privacy Preservin Properties of Random Data PerturbationTechniques” Proceedings of the Third IEEE International Conference on Data Mining, pages 43-56, 2003
[7] Krishnamoorthy Siva Kumar, “SpectralFiltering Ttechnique Method” Proceedings of the Third IEEE International Conference on Data Mining, pages 40-48, 2003.
[8] Hillol Kargupta, Kun Liu, Souptik Datta, and Jessica Ryan Krishnamoorthy Siva Kumar:” Homeland Security and Privacy Sensitive Data Mining from Multi-Party distributed Resources” The IEEE International Conference on Fuzzy Systems pages 727-764, 2003
[9] Jaideep Vaidya Chris Clifton , “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, In ACM SIGMOD Workshop on Research Issues Data Mining and Knowledge Discovery, pages 639-644, June 2002
[10] LiWu Chang and Ira S. Moskowitz, Parsimonious downgrading and decisiontrees applied to the inference problem, In Proceedings of the 1998 New SecurityParadigms Workshop, pages 82–89, 1998.
[11] Dr.Kenneth Collien, Dr Carreyand Mr.Donald Sautter, “A Perspective of Data Mining Techniques”,1998.
[12] Rakesh Agrawal, Tomasz Imielinski, Arun Swami.Mining “Association Rules Between Sets Of Items In Large Databases”. Proc. of the ACM SIG-MOD Conference on Management of Data 216, Washington, D.C., May 1993
[13] Alexandre Evfimievski and Tyrone Grandison, “Privacy Preserving Data Mining” at IBM Almaden Research Center, 2007.
[14] Tzung-Pei Hong, Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan, ”Evolutionary privacy-preserving data mining”, 19-23 Sept. 2010.
[15] Er.M.R.Arun Venkatesh, Bharath University, Chennai, “Privacy-Preserving Updates to Anonymous and Confidential Database”, June-2012.
[16] Justin Zhan from Carnegie Mellon University, USA, “Privacy- Preserving Collaborative Data Mining”, 2008.
[17] Benjamin C.M. Fung, Ke Wang, and Philip S. Yu, Fellow, IEEE, “Anonymizing Classification Data for Privacy Preservation”, may 2007.
[18] Keke Chen, Member, IEEE, and Ling Liu, Senior Member, IEEE, “Privacy-Preserving Multiparty Collaborative Mining with Geometric Data Perturbation”, Dec-2009.
[19] Agrawal, R. & Srikant, R., “Privacy Preserving Data Mining. In Proc. of ACM SIGMOD”, Conference on Management of Data (SIGMOD’00), Dallas, TX, 2000.