The week's pick
Reseach Article
A Novel Framework to Improve the Quality of Additive Perturbation Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 6 |
| Year of Publication: 2011 |
| Authors: P.Kamakshi, Dr. A.Vinaya Babu |
10.5120/3643-5089
|
P.Kamakshi, Dr. A.Vinaya Babu . A Novel Framework to Improve the Quality of Additive Perturbation Technique. International Journal of Computer Applications. 30, 6 ( September 2011), 37-40. DOI=10.5120/3643-5089
Abstract
Data mining tool discovers valuable knowledge [1] from the huge volumes of data. The knowledge discovered is treated as an important asset and constructive feature for the growth of business. Inspite of its significant analytical capability the data mining tools have some limitations to its capabilities. The data mining techniques not only derive interesting patterns but can also reveal highly sensitive information available in the database. Publicizing such data for analysis purpose opens new threats to individual privacy. The threat to privacy [12] due to data mining is the highly concerned issue from the society. Privacy preserving data mining [2] is new research area in data mining, which presents solutions to privacy problems that occur during the data mining process. The main motive of PPDM technique is to obtain valid data mining results without access to the original sensitive information. In this paper we discussed the problems associated with privacy preservation of sensitive information due to basic randomized additive perturbation technique. We proposed a novel framework to enhance the performance and quality of basic additive perturbation technique. This framework not only preserves the privacy of sensitive information, but also gives modified database valid for various analysis purpose.
References
- Han J., Kamber M., 2006.Data Mining: Concepts and Techniques, Beijing: China Machine Press.
- Agrawal R., Srikant R.,2000.Privacy-Preserving Data Mining,ACM SIGMOD Record.
- Kargupta H., Datta S.,Wang Q., Sivakumar K., 2003.On the Privacy Preserving Properties of Random Data Perturbation Techniques, In Proceedings of the 3rd International Conference on Data Mining.
- Bacher, J., Bender, S., and Brand, R. 2001.Re-identifying Register Data by Survey Data: An Empirical Study .Ppresented at the UNECE Workshop On Statistical Data Editing.
- Evfimievski, A., Gehrke, J., and Srikant, R.2003.Limiting Privacy Breaches in Privacy Preserving Data Mining. In Pro ceedings of ACM PODS Conference.
- Agrawal R.2004. Data Privacy. In Proceedings of 8th European Conference on Principles and Practice of Knowledge Discovery in Databases.
- Fienberg S. 2006. Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science,
- Dwork C.and Nissim K. 2004. Privacy-preserving datamining on vertically partitioned databases.In Proceedings of CRYPTO.
- Agrawal, R., Evfimievski, A.and Srikant R. 2003. Information sharing across private databases. In Proceedings of ACM SIGMOD international conference on Management of data..
- Liu L., Thuraisingham B., Kantarcioglu M., and Khan L. 2005.An Adaptable Perturbation Model of Privacy Preserving Data Mining. In Proceeding of ICDM Workshop on Privacy and Security Aspects of Data Mining
- Vaidya J. and Clifton C. 2003. Leveraging the" Multi" in secure multi-party computation. Workshop On Privacy In The Electronic Society.
- Kantarcioglu, M. , Jin , J. and Clifton C. 2004. When do data mining results violate privacy?. In Proceedings ACM SIGKDD international conference on Knowledge discovery and data mining,
- Rakesh A., Evfimievski A. and Srikant R.2003. Information sharing across private databases. In Proceedings of International conference On Management of Data.
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