Privacy Preservation of Data using Factorization  
  Authors : N. Maheswari; Ankita Adhawale; Amol Bhausaheb Wale

 

Preserving the privacy of the personal information, valuable company data, important records of organizations become the difficult task day by day. Using data mining technique in many applications, there are various problems regarding security, privacy issue comes front. There are lots of techniques used to maintain privacy of data records. Transformation of data values is one of the well known methods to maintain the privacy of data records. This article presents LU-factorization method to maintain the privacy of data records. Clustering techniques had performed on the original and the distorted data sets. Performance measures have been used to evaluate the distorted data records with original data records. The experimental result shows that the LU factorization method maintains the balance between privacy and accuracy. The accuracy of the clustering has been measured and it produced acceptable results.

 

Published In : IJCAT Journal Volume 3, Issue 5

Date of Publication : June 2016

Pages : 326-331

Figures :04

Tables : 01

Publication Link :Privacy Preservation of Data using Factorization

 

 

 

N. Maheswari : School of Computing Science and Engineering VIT University, Chennai

Ankita Adhawale : School of Computing Science and Engineering VIT University, Chennai

Amol Bhausaheb Wale : School of Computing Science and Engineering VIT University, Chennai

 

 

 

 

 

 

 

Privacy Preserving, Data Distortion, Data Mining, Clustering

This article proposes a new technique for privacy preservation using LU-Factorization method. The original datasets are transformed and the privacy measures are applied to know the percentage of privacy preservation. By applying the privacy measure to the existing system like SVD and PCA and comparing the results with proposed method, it has been concluded that LU-Factorization is better in preserving the privacy of the dataset. Privacy measures and the misclassification error results show the balance between clustering accuracy and privacy.

 

 

 

 

 

 

 

 

 

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