Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

2013 ◽  
Author(s):  
Feng Zhang ◽  
Chunming Rong ◽  
Gansen Zhao ◽  
Jinxia Wu ◽  
Xiangning Wu
Keyword(s):  
Association Rules ◽  
Privacy Preserving ◽  
Association Rules Mining ◽  
Partitioned Data ◽  
Distributed Association

An Improved Multi-Parameters Perturbation Privacy Preserving Association Rules Mining Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 2629-2632
Author(s):  
Jie Liu ◽  
Tian Qi Li ◽  
Jian Pei Zhang
Keyword(s):  
Association Rules ◽  
Original Data ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Perturbation ◽  
First Line ◽  
Time Efficiency ◽  
Association Rules Mining ◽  
Original Algorithm ◽  
Parameters Perturbation

Multi-parameters data perturbation method is a kind of original data perturbation methods for privacy preserving association rules mining. However, the time-efficiency of restoring the frequent itemsets in multi-parameters perturbation algorithm is still not high.One method is proposed in this paper to improve the time efficiency of multi-parameters randomized perturbation algorithm according to the characteristics of the model to restore frequent itemsets. The method improves the time efficiency by getting the elements of the first line of the inversed matrix of transformation matrix. Finally, both theoretical analysis and experimental results show that the improved algorithm is more time-efficient and space-efficient than the original algorithm.

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