scholarly journals Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

2006 ◽  
Vol 18 (1) ◽  
pp. 92-106 ◽  
Author(s):  
Kun Liu ◽  
H. Kargupta ◽  
J. Ryan
Keyword(s):  
Data Mining ◽  
Random Projection ◽  
Privacy Preserving ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Perturbation

scholarly journals Partition Based Perturbation for Privacy Preserving Distributed Data Mining

2017 ◽  
Vol 17 (2) ◽  
pp. 44-55 ◽  
Author(s):  
M. Antony Sheela ◽  
K. Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Threshold Level ◽  
Third Party ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Perturbation ◽  
Private Data ◽  
Partitioned Data ◽  
Vertically Partitioned Data ◽  
The Mean

Abstract Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes.

scholarly journals Tools for privacy preserving distributed data mining

2002 ◽  
Vol 4 (2) ◽  
pp. 28-34 ◽  
Author(s):  
Chris Clifton ◽  
Murat Kantarcioglu ◽  
Jaideep Vaidya ◽  
Xiaodong Lin ◽  
Michael Y. Zhu
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Distributed Data Mining ◽  
Distributed Data

scholarly journals Hiding Sensitive Association Rules over Privacy Preserving Distributed Data Mining

2014 ◽  
Vol 9 (1) ◽  
pp. 59-72
Author(s):  
Alaa Khalil Jumaa ◽  
Sufyan T. F. Al-Janabi ◽  
Nazar Abedlqader Ali
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Distributed Data Mining ◽  
Distributed Data
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