CFP last date
22 April 2024
Reseach Article

Sensitive Outlier Protection in Privacy Preserving Data Mining

by S.Vijayarani, S.Nithya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 33 - Number 3
Year of Publication: 2011
Authors: S.Vijayarani, S.Nithya
10.5120/4000-5667

S.Vijayarani, S.Nithya . Sensitive Outlier Protection in Privacy Preserving Data Mining. International Journal of Computer Applications. 33, 3 ( November 2011), 19-27. DOI=10.5120/4000-5667

@article{ 10.5120/4000-5667,
author = { S.Vijayarani, S.Nithya },
title = { Sensitive Outlier Protection in Privacy Preserving Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 33 },
number = { 3 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume33/number3/4000-5667/ },
doi = { 10.5120/4000-5667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:19:12.394042+05:30
%A S.Vijayarani
%A S.Nithya
%T Sensitive Outlier Protection in Privacy Preserving Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 33
%N 3
%P 19-27
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. Privacy preserving data mining is a latest research area in the field of data mining which generally deals with the side effects of the data mining techniques. Privacy is defined as “protecting individual’s information”. Protection of privacy has become an important issue in data mining research. Sensitive outlier protection is novel research in the data mining research field. Clustering is a division of data into groups of similar objects. One of the main tasks in data mining research is Outlier Detection. In data mining, clustering algorithms are used for detecting the outliers efficiently. In this paper we have used four clustering algorithms to detect outliers and also proposed a new privacy technique GAUSSIAN PERTURBATION RANDOM METHOD to protect the sensitive outliers in health data sets.

References
  1. Aggarwal C.C, Yu P.S., “Models and Algorithms: Privacy-Preserving Data Mining”, Springer, 2008
  2. Ajay Challagalla,S.S.Shivaji Dhiraj ,D.V.L.N Somayajulu,Toms Shaji Mathew,Saurav Tiwari,Syed Sharique Ahmad “ Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods” 2010 34th Annual IEEE Computer Software and Applications Conference Workshops.
  3. Al-Zoubi, M., Al-Dahoud, A. and Yahya, A.A. (2010) “New Outlier Detection Method Based on Fuzzy Clustering, WSEAS Transactions on Information Science and Applications”, Vol. 7, Issue 5
  4. Al-Zoubi, M. (2009) “An Effective Clustering-Based Approach for Outlier Detection”, European Journal of Scientific Research.
  5. “An Effective Clustering-Based Approach for Outlier Detection”, Moh’d Belal Al- Zoubi, European Journal of Scientific Research, ISSN 1450-216X Vol.28 No.2 (2009).
  6. Antonio Loureiro, Luis Torgo, and Carlos Soares, “Outlier Detection Using Clustering Methods: a data cleaning application”, in Proceedings of the Data Mining for Business Workshop, 2009.
  7. Elisa Bertino , Dan Lin and Wei Jiang, “A Survey of Quantification of Privacy Preserving Data Mining Algorithms”, in Privacy-Preserving Data Mining (Models and Algorithms), Charu C. Aggarwal and Philip S. Yu (Eds.), Springer-Verlag, 2008.
  8. E. M. Knorr and R. T. Ng. “Algorithms for mining distance based outliers in large datasets”. In Proceedings of 24th International Conference on Very Large Data Bases (VLDB 1998), New York City, NY, USA, Aug.24-27 1998.
  9. Friedman A., Wolff R., Schuster A. “Providing k -anonymity in data mining”, The VLDB Journal , Vol.17 ,2008.
  10. Jaideep Vaidya, Chris Clifton, W.Lafayette “Privacy Preserving Data Mining” Springer 2006.
  11. Jeffrey W. S., “Data mining: An overview”, CRS report RL 31798.
  12. Jiang, S. And An, Q. (2008), “Clustering Based Outlier Detection Method”, Fifth International Conference on Fuzzy Systems and Knowledge Discovery.
  13. Jiawei Han, Micheline Kamber, “Data Mining: Concepts and Techniques”, 2nd edition, Morgan Kaufmann, 2006.
  14. John Peter.S., Department of computer science and research center St.Xavier’s College, Palayamkottai, “An Efficient Algorithm for Local Outlier Detection Using Minimum Spanning Tree”, International Journal of Research and Reviews in Computer Science (IJRRCS), March 2011.
  15. Jyothsna R.Nayak and Diane J.Cook, “Approximate Association Rule Mining”, the Florida AI Research Society Conference FLAIRS, 2001.
  16. Knurs, E.M. and Ng, R.T. (1998) Algorithms for mining Distance-based outliers in Large Datasets, VLDB
  17. Liu, H., Shah, S. and Jiang, W. (2004) “On-line outlier detection and data cleaning”, Computers and Chemical Engineering .
  18. Loureiro,A., Torgo, L. and Soares, C. (2004) “Outlier Detection using Clustering Methods: a Data Cleaning Application”, in Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. Bonn, Germany.
  19. Mahfouz, M.A. and Ismail, M.A. (2009)” Fuzzy relatives of the CLARANS algorithm with application to text clustering”, World Academy of Science, Engineering and Technology.
  20. Murugavel. P. et al, “Improved Hybrid Clustering And Distance-Based Technique for Outlier Removal”, International Journal on Computer Science and Engineering (IJCSE), 1 JAN 2011
  21. Poovammal E., Ponnavaikko M., “An Improved Method for Privacy Preserving Data Mining”, International Advance Computing Conference, 2009.
  22. Samarati P, Sweeney L. Protecting “Privacy when Disclosing Information: k-Anonymity and its Enforcement Through Generalization and Suppression”. IEEE Symp. on Security and Privacy, 1998.
  23. Sheng-yi Jiang, Qing-bo- An, “Clustering-Based Outlier Detection Method”, Fifth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD'08, 2008.
  24. Sweeney L. “AI Technologies to Defeat Identity Theft Vulnerabilities”. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005.
  25. Sweeney L. “Privacy Technologies for Homeland Security”. Testimony before the Privacy and Integrity Advisory Committee of the Department of Homeland Security, Boston, MA, June 15, 2005.
  26. Zenyou He *,Xiaofei Xu, Shenchun Deng ., “Discovering Cluster Based Local Outliers”, Department of computer science and Engineering, Harbin Institute of Technology, Harbin 150001,P.R.China.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Privacy Clustering PAM CLARA CLARANS ECLARANS Outlier Detection Gaussian Perturbation Random Method