The week's pick
Random Articles
Reseach Article
SVD based Data Transformation Methods for Privacy Preserving Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 78 - Number 3 |
| Year of Publication: 2013 |
| Authors: M. Naga Lakshmi, K Sandhya Rani |
10.5120/13473-1157
|
M. Naga Lakshmi, K Sandhya Rani . SVD based Data Transformation Methods for Privacy Preserving Clustering. International Journal of Computer Applications. 78, 3 ( September 2013), 39-43. DOI=10.5120/13473-1157
Abstract
Nowadays privacy issues are major concern for many government and other private organizations to delve important information from large repositories of data. Privacy preserving clustering which is one of the techniques emerged to addresses the problem of extracting useful clustering patterns from distorted data without accessing the original data directly. In this paper two hybrid data transformation methods are proposed for privacy preserving clustering in centralized database environment based on Singular Value Decomposition (SVD). In hybrid method one, SVD and rotation data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and independent component analysis are used as a combination to obtain the distorted dataset. In SVD the data is analyzed in different perspectives to retain important information. Higher order statistics which contains more important information is utilized in independent component analysis. Experimental results demonstrate that the proposed methods are efficiently protects the private data of individuals and retains the important information for clustering analysis.
References
- S. R. M. Oliveria, Data Transformation for Privacy-Preserving Data Mining, PhD thesis, University of Alberta, 2005.
- Clayton W, "Ethical, Legal and Social Implications of Genomic Medicine", New England Journal of Medicine, vol. 349, no. 6, pp. 562-569, 2003.
- A. F. Westin, Freebies and privacy: what net users think, Technical report, Opinion Research Corporation, July 1999, Available from http://www. privacyexchange. org/iss/surveys/sr990714. html
- E. Bernito, I Fovino, and L. Provenza, A framework for evaluating privacy preserving data mining algorithms Data Mining and Knowledge Discovery, vol. 29, no 2, pp. 439-450, 2000.
- S. Xu, J. Zhang, D. Han, J. Wang, Singular value decomposition based data distortion strategy for privacy protection Knowledge and Information Systems, vol. 10, no. 3, pp. 348-361,2007.
- H. Polat, W. Du, SVD-based collaborating filtering with privacy, In the 20th ACM Symposium on applied computing, Track on E-commerce Technologies, Santa Fe, New Mexico, USA. March 13-17, 2005.
- J. Wang, J. Zhan, and J. Zhang, Towards real-time performance of data value hiding for frequent data updates, In Proceedings of the IEEE International conference on Granular Computing. IEEE Computer Society, 2008, pp. 606-611.
- Jie W, Zhong WXu S, and Zhang J, Selective data distortion via structural partition and SSVD for privacy preservation in proceedings of International conference on Information and knowledge Engineering . pp-:114-120, CSERA press, Las Vegas, Nevada, USA, June.
- N. Maheswari, K. Duraiswamy, CLUST-SVD: Privacy Preserving Clustering in Singular value Decomposition World, Journal of Modeling and Simulation, Vol. 4 (2008), No. 4, pp 250-256.
- Guang Li,Yadong Wang A Privacy- Preserving Data Mining Method based on SingularValue Decomposition and Independent Component Analysis In proceeding of Data Science Journal,Volume9,16 February 2011.
- M. Kalitha, D. K. Bhattacharyya, M. Dutta (2008), Privacy Preserving Clustering-A Hybrid Approach ADCOM 2008.
- Guang Li, Yadong Wang, "A Privacy-Preserving Classification Method Based on Singular Value Decomposition", The International Arab Journal of Information Technology, Vol. 9, No. 6, November 2012.
- Hyvarinen, A, Karhunen, J, & Oja, E (2001) Independent Component Analysis, Hoboken, New Jersey, US: John Wiley & Sons Inc.
- Machine Learning Repository http://archive . ics. uci. edu/ml/datasets. html.
- Stanley R. M. Oliveria, Osmar R. Zaiane, Privacy Preserving Clustering By Data Transformation. Proceedings of the 18th Brazilian Symposium on Databases, 2003. 304-318.
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
Index Terms
Keywords