An Enhanced Scheme for Privacy-Preserving Association Rules Mining on Horizontally Distributed Databases

2012 ◽  
Author(s):  
Xuan Canh Nguyen ◽  
Hoai Bac Le ◽  
Tung Anh Cao
Keyword(s):  
Association Rules ◽  
Distributed Databases ◽  
Privacy Preserving ◽  
Association Rules Mining

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.

scholarly journals ASSOCIATION RULES MINING IN BIG DATA

2018 ◽  
pp. 25-32 ◽  
Author(s):  
Nataliya Shakhovska ◽  
Roman Kaminskyy ◽  
Eugen Zasoba ◽  
Mykola Tsiutsiura
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Distributed Databases ◽  
Data Sources ◽  
Data Types ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Data Definition ◽  
Subject Areas ◽  
Asymptotic Complexity

The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.

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