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Prototype of Clustering and Classification Model for Privacy Preservation using Single Vector Decomposition

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Richa Lodhi, Anil Suryavanshi

Richa Lodhi and Anil Suryavanshi. Article: Prototype of Clustering and Classification Model for Privacy Preservation using Single Vector Decomposition. International Journal of Computer Applications 133(11):23-26, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Richa Lodhi and Anil Suryavanshi},
	title = {Article: Prototype of Clustering and Classification Model for Privacy Preservation using Single Vector Decomposition},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {11},
	pages = {23-26},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The single value decomposition technique divides the data of different parties during the process of privacy preservation. The process of single value decomposition implied in the form of clustering and classification. The combined process of clustering and classification called prototype mode for sharing privacy preservation. The utility of vector decomposition in this model is selection of data in different parties for the process of maintain the raw information. In this paper proposed a prototype model for privacy preservation and improved the efficiency of data utility and accuracy of data recovery during the process of privacy. The proposed model implements in MATLAB software and used some standard dataset for evaluation of performance. The proposed model is very efficient in compression of KPPDM model.


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Clustering, classification, sample selection, PPDM