Privacy Preserving in Association Rules Mining with VPA Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 3060-3063 ◽  
Author(s):  
Yi Tao Zhang ◽  
Wen Liang Tang ◽  
Cheng Wang Xie ◽  
Ji Qiang Xiong
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Privacy Preserving Data Mining ◽  
Association Rules Mining ◽  
Synthetic Datasets

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.

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

2014 ◽  
Vol 23 (05) ◽  
pp. 1450004 ◽  
Author(s):  
Ibrahim S. Alwatban ◽  
Ahmed Z. Emam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Comprehensive Survey ◽  
New Research

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

scholarly journals Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

2020 ◽  
Vol 3 (2) ◽  
pp. 89
Author(s):  
Adie Wahyudi Oktavia Gama ◽  
Ni Made Widnyani
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Simple Modification ◽  
Scanning Process ◽  
Iteration Methods ◽  
Short Time

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;

Association Rule Hiding in Privacy Preserving Data Mining

2021 ◽  
pp. 963-986
Author(s):  
S. Vijayarani Mohan ◽  
Tamilarasi Angamuthu
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Hidden Information ◽  
Privacy Preserving Data Mining ◽  
Marketing Information ◽  
Mining Association Rule

This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.

Research and Application of an Enhanced Data Mining Algorithm in Virtual Manufacturing Technology

2011 ◽  
Vol 299-300 ◽  
pp. 840-843
Author(s):  
Yu Jun Tong ◽  
Jun Zhou ◽  
Wen Ge Xie ◽  
Dan Jia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Manufacturing Technology ◽  
Virtual Manufacturing ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Multiple Minimum Supports ◽  
Important Branch

Association rules mining is an important branch of data mining. Apriori algorithm is a classical algorithm of mining association rules. Based on the original Apriori algorithm an improved Apriori algorithm is analyzed according to the multiple minimum supports and support difference constraint. An experiment has been conducted and the results showed that the new algorithm can not only mine out the association rules to meet the demands of multiple minimum supports, but also mine out the rare but potentially profitable items’ association rules.

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