The week's pick
Reseach Article
Security Information Hiding in Data Mining on the bases of Privacy Preserving Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 15 |
| Year of Publication: 2010 |
| Authors: Varun Yadav, Richa Jindal |
10.5120/324-492
|
Varun Yadav, Richa Jindal . Security Information Hiding in Data Mining on the bases of Privacy Preserving Technique. International Journal of Computer Applications. 1, 15 ( February 2010), 46-49. DOI=10.5120/324-492
Abstract
Data mining has attracted a great deal of information industry and in society as a whole in recent years, due to the wide availability of huge amount of data and the imminent need for such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection and customer retention, to production control and science exploration. With and more information accessible in electronic forms and available on the web, and increasingly powerful data mining tools being developed and put into use, data mining may pose a threat to our privacy and data security .The real privacy concerns are with unconstrained access of individual records, like credit card, banking applications, customer ID, which must access privacy sensitive information. In this paper we investigate the issue of data mining, as data shared before mining the means to shield it with Unified Modeling Language diagrams. Describing the privacy preserving definition, problem statement privacy preserving data mining technique, Architecture of the proposed work. we propose an amalgamated scaffold for Privacy Preserving Data Mining that ensures that the mining process will not trespass Privacy up to a certain degree of security.
References
- Chris Clifton and Donald Marks, Security and privacy implications of data mining, In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1996), 15–19.
- Daniel E. O’Leary, Knowledge Discovery as a Threat to Database Security, In Proceedings of the 1st International Conference on Knowledge Discovery and Databases (1991), 107–516.
- Nabil Adam and John C. Wortmann, Security- Control Methods for Statistical Databases: A Comparison Study, ACM Computing Surveys 21 (1989), no. 4, 515–556.
- R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu. Hippocratic Databases. In Proc. Of the 28th Conference on Very Large Data Bases, Hong Kong, China, August 2002.
- L. Brankovic and V. Estivill-Castro. Privacy Issues in Knowledge Discovery and Data Mining. In Proc. of Australian Institute of Computer Ethics Conference (AICEC99), Melbourne, Victoria, Australia, July 1999.
- S. R. M. Oliveira and O. R. Zaiane. Foundations for an Access Control Model for Privacy Preservation in Multi- Relational Association Rule Mining. In Proc. of the IEEE ICDM Workshop on Privacy, Security, and Data Mining, pages 19 -26, Maebashi City, Japan, December 2002
- C. Clifton, W. Du, M. Atallah, M. Kantarcio_glu, X. Lin, and J. Vaidya. Distributed Data Mining to Protect Information Privacy. Proposal to the National Science Foundation, December 2001.
- C. Clifton. Using Sample Size to Limit Exposure to Data Mining. Journal of Computer Security, 8(4):281-307, November 2000.
- L. Sweeney. k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and
- C. Farkas and S. Jajodia. The Inference Problem: A Survey. SIGKDD Explorations, 4(2):6{11, December 2002.Knowledge-Based Systems, 10(5):557-570, 2002.
- L. Willenborg and T. D. Waal. Statistical Disclosure Control in Practice. Springer- Verlag, 1996.
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