TY  - JOUR
T1  - Privacy Preserving Data Mining Using Sliced Data for Classification Technique
AU - Susan, V. Shyamala AU - Christopher, T. 
JO  - International Journal of Soft Computing
VL  - 10
IS  - 6
SP  - 468
EP  - 475
PY  - 2015
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2015.468.475
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2015.468.475
KW  - Privacy preservation data publishing
KW  -Radial Basis Function (RBF)
KW  -Multiple Linear Regression (MLR)
KW  -classification technique
KW  -India
AB  - Privacy preservation in data publishing is the major topic of research in the field of data security. Data
publication in privacy preservation provides methodologies for publishing useful information; simultaneously
the privacy of the sensitive data has to be preserved. There has been little research addressing how to
effectively use the preserved data for data mining in general and for distributed data mining in particular. Here,
we propose a new approach for building classifiers using Radial Basis Function (RBF) and Multiple Linear
Regression (MLR) by employing sliced data as uncertain data. Use of probability distribution employed in the
slicing approach was replaced by classification techniques to enable modeling for sliced data. InRBF, the sliced
data is sent into the input layer, the activation function is executed by the hidden lauer and output layer
produces classified data. In the same manner, MLR calculates approximate value of one or more sliced data
responses on the basis of certain predictors. Results from the experiments show that these techniques show
better performance in comparision with other classification approaches.
ER  -