Preserving Privacy Using Geometric Transformation in Data Stream

  Darshini U. Patel,  Maulik Dhamecha

Data stream can be characterize as a continuously changing sequence of data that come over the framework constantly for storing or handling. Illustrations identified with data streams incorporate PC system activity, web inquiries and sensor information and so on. The proprietors of the information may not be willing to uncover the accurate estimations of their data because of a few reasons, most likely privacy concern. In this way, for protecting information security amid data mining, prevacy preserving mining issue has been generally studied over and even numerous methods have been proposed. Yet at the same time the systems that have been intended for protection safeguarding data mining are for customary static information sets just and are not for data streams. So this issue for privacy preserving of data streams mining is required for the time. This paper essentially centered around strategies for Principal Component Analysis (PCA) based change for stream information utilizing Massive Online Analysis (MOA). The clustering accuracy is verging on equivalent to the first dataset utilizing perturbe data.

Keywords- Data Stream, Geometric Transformation, Data Perturbation, Random Function

Unique Identification Number - IJEDR1503112
Page Number(s) - 1-4
Pubished in - Volume 3 | Issue 3 | September 2015
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Darshini U. Patel,  Maulik Dhamecha,   "Preserving Privacy Using Geometric Transformation in Data Stream", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 3, pp.1-4, September 2015, Available at :http://www.ijedr.org/papers/IJEDR1503112.pdf