A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree

Author(s):  
Chunhua Su ◽  
Kouichi Sakurai
2011 ◽  
Vol 130-134 ◽  
pp. 2629-2632
Author(s):  
Jie Liu ◽  
Tian Qi Li ◽  
Jian Pei Zhang

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.


2012 ◽  
Vol 263-266 ◽  
pp. 3060-3063 ◽  
Author(s):  
Yi Tao Zhang ◽  
Wen Liang Tang ◽  
Cheng Wang Xie ◽  
Ji Qiang Xiong

A VPA algorithm is proposed to mining the association rules in the privacy preserving data mining, where data is vertically partitioned. The VSS protocol was used to encrypt the vertically data, which was owned by different parties. And the private comparing protocol was adopted to generate the frequent itemset. In VPA the ID numbers of the recordings were employed to keep the consistency of the data among different parties, which were saved in ID index array. The VPA algorithm can generate association rules without violating the privacy. The performance of the scheme is validated against representative real and synthetic datasets. The results reveal that the VPA algorithm can do the same in finding frequent itemset and generating the consistent rules, as it did in Apriori algorithm, in which the data were vertically partitioned and totally encrypted.


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