A sanitization approach for privacy preserving data mining on social distributed environment

2019 ◽  
Vol 11 (7) ◽  
pp. 2761-2777 ◽  
Author(s):  
P. L. Lekshmy ◽  
M. Abdul Rahiman
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

Collusion-Free Privacy Preserving Data Mining

2010 ◽  
Vol 6 (4) ◽  
pp. 30-45 ◽  
Author(s):  
M. Rajalakshmi ◽  
T. Purusothaman ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

Collusion-Free Privacy Preserving Data Mining

2012 ◽  
pp. 269-284
Author(s):  
T. Purusothaman ◽  
M. Rajalakshmi ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Distributed Association

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

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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