Privacy Preserving Data Mining Using General Regression Auto-Associative Neural Network: Application to Regression Problems

Author(s):  
Vadlamani Ravi ◽  
Amit Yadav
2011 ◽  
Vol 403-408 ◽  
pp. 920-928 ◽  
Author(s):  
Nekuri Naveen ◽  
V. Ravi ◽  
C. Raghavendra Rao

In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.


2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.


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