TY  - JOUR
T1  - Enhanced Applicability of Privacy Preservation for Perturbed Data in Multi-Partitioned Data Set
AU - Prakash, V.S. AU - Shanmugam, A. 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 2
SP  - 109
EP  - 116
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.109.116
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.109.116
KW  - Data mining
KW  -privacy
KW  -security
KW  -multi-partitioned dataset
KW  -data perturbation
AB  - The perturbation technique has been widely considered for privacy preserving 
  in data mining for different datasets. Generally, multi-partitioned datasets 
  comprises of both vertical and horizontal data sets which is being a current 
  demand of e-Business and e-Commerce data mining environment. In perturbation 
  process, arbitrary noise from a recognized distribution is processed as privacy 
  susceptible data, prior the data is thrown to the data miner. Consequently, 
  the data miner rebuilds estimation to the unique data distribution from the 
  perturbed data and exercises the renovated delivery for data mining principles. 
  Owing to the count of noise, loss of information versus conservation of privacy 
  is a constant transaction in the perturbation based techniques. The question 
  is to what level the users are disposed to cooperate with their privacy? This 
  is a preference that amends from individual to individual. To assess a tradeoff 
  among data privacy and simplicity of individual’s data, the first research 
  is to describe the data perturbation technique with validation and authentication. 
  Diverse individuals may have diverse approaches towards confidentiality, based 
  on traditions and cultures. Unfortunately, the earlier perturbation based privacy 
  preserving data mining techniques do not permit the individuals to decide their 
  preferred privacy levels. This is a negative aspect as privacy is an individual 
  choice. In this study, researchers propose an individually adaptable perturbation 
  model which enables the individuals to choose their own privacy levels. The 
  effectiveness of the proposed model lies is the enhancement of the Applicability 
  of Privacy Preservation for Perturbed Data in Multi-partitioned datasets (APPDM) 
  demonstrated by diverse experiments conducted on both synthetic and real-world 
  data sets. Based on the experimental evaluation, researchers propose a simple, 
  valuable and resourceful method to construct data mining models from perturbed 
  data and enhance the process of privacy preservation.
ER  -