Towards Privacy-Preserving Data Mining in Online Social Networks: Distance-Grained and Item-Grained Differential Privacy

2016 ◽  
pp. 141-157 ◽  
Author(s):  
Shen Yan ◽  
Shiran Pan ◽  
Yuhang Zhao ◽  
Wen-Tao Zhu
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Online Social Networks ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining

scholarly journals Analyzing and Performing Privacy Preserving Data Mining on Medical Databases

2016 ◽  
Vol 9 (17) ◽  
Author(s):  
D. Aruna Kumari ◽  
Y. Vineela ◽  
T. Mohan Krishna ◽  
B. Sai Kumar
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining ◽  
Medical Databases

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

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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