scholarly journals Efficient Method in Association Rule Hiding for Privacy Preserving with Data Mining Approach

2019 ◽  
Vol 24 (1) ◽  
pp. 47-50
Author(s):  
Kurapati Praveena ◽  
Gudla Sirisha ◽  
Satukumati Babu ◽  
Panchala Rao
Keyword(s):  
Data Mining ◽  
Efficient Method ◽  
Association Rule ◽  
Privacy Preserving ◽  
Data Mining Approach

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.

An Association Rule on eDisiplin Case Study: An Educational Data Mining Approach

2018 ◽  
Vol 24 (3) ◽  
pp. 1872-1875 ◽  
Author(s):  
Mustafa Man ◽  
Wan Aezwani Wan Abu Bakar ◽  
Ily Amalina Ahmad Sabri
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Educational Data Mining ◽  
Data Mining Approach

DATA MINING APPROACH FOR ADVANCEMENT OF “ASSOCIATION RULE MINING” USING “R PROGRAMMING”

2016 ◽  
Vol 10 (4) ◽  
pp. 6
Author(s):  
VIJ RAHUL KUMAR ◽  
KALRA PARVEEN ◽  
JAWALKAR C.S. ◽  
◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Approach ◽  
R Programming

Recent Advances in Preserving Privacy Data Mining

2013 ◽  
Vol 798-799 ◽  
pp. 541-544
Author(s):  
Gao Ming Yang ◽  
Jing Zhao Li ◽  
Shun Xiang Zhang
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Evaluation Criteria ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Algorithm Performance ◽  
Data Distortion ◽  
Detailed Evaluation

A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.

Survey on Privacy Preserving Association Rule Data Mining

2017 ◽  
Vol 4 (2) ◽  
pp. 63-80 ◽  
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Performance Metrics ◽  
Relevant Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Research Issues ◽  
Data Mining Techniques

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

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