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.

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.

Preserving Privacy in Mining Quantitative Associations Rules

2011 ◽  
pp. 310-326
Author(s):  
Madhu V. Ahluwalia ◽  
Aryya Gangopadhyay ◽  
Zhiyuan Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Input Data ◽  
Academic Community ◽  
Discrete Wavelet ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

scholarly journals Privacy Preserving with Association Rule Mining using Evolutionary Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 11893-11899
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Multiple Data ◽  
Modified Algorithm

Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance

Sensitive Items in Privacy Preserving — Association Rule Mining

2008 ◽  
Vol 07 (01) ◽  
pp. 31-35
Author(s):  
K. Duraiswamy ◽  
N. Maheswari
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Data Input ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

Sensitivity Association Rule Mining using Weight based Fuzzy Logic

2017 ◽  
Vol 9 (2) ◽  
pp. 1 ◽  
Author(s):  
Meenakshi Bansal ◽  
Dinesh Grover ◽  
Dhiraj Sharma
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Side Effects ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Experimental Results ◽  
Rule Mining

Mining of sensitive rules is the most important task in data mining. Most of the existing techniques worked on finding sensitive rules based upon the crisp thresh hold value of support and confidence which cause serious side effects to the original database. To avoid these crisp boundaries this paper aims to use WFPPM (Weighted Fuzzy Privacy Preserving Mining) to extract sensitive association rules. WFPPM completely find the sensitive rules by calculating the weights of the rules. At first, we apply FP-Growth to mine association rules from the database. Next, we implement fuzzy to find the sensitive rules among the extracted rules. Experimental results show that the proposed scheme find actual sensitive rules without any modification along with maintaining the quality of the released data as compared to the previous techniques.

A Survey for Association Rule Mining in Data Mining

2017 ◽  
Vol 7 (8) ◽  
pp. 245
Author(s):  
Shivangee Agrawal ◽  
Nivedita Bairagi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
Base Level ◽  
Significant Research ◽  
Total Usage

Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

Research on Privacy Preserving Data Mining Association Rules Protocol

2013 ◽  
Vol 756-759 ◽  
pp. 1661-1664 ◽  
Author(s):  
Xiao Ming Zhu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Rule Mining ◽  
Individual Values ◽  
Privacy Preserving Data Mining ◽  
Future Data

Privacy preserving in data mining is a significant direction. There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product. Then, the data mining and secure multiparty computation are briefly introduced. And proposes an implementation for privacy preserving mining protocol based secure multiparty computation protocol.

Association Rule Hiding in Privacy Preserving Data Mining

2018 ◽  
Vol 12 (3) ◽  
pp. 141-163 ◽  
Author(s):  
S. Vijayarani Mohan ◽  
Tamilarasi Angamuthu
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Research Directions ◽  
Marketing Information

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.

Constraint-Based Association Rule Mining

2011 ◽  
pp. 307-312 ◽  
Author(s):  
Carson Kai-Sang Leung
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Computational Cost ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
The Subject ◽  
User Focus ◽  
High Computational Cost

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.

Association Rule and Quantitative Association Rule Mining among Infrequent Items

2010 ◽  
pp. 15-32 ◽  
Author(s):  
Ling Zhou ◽  
Stephen Yau
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Transactional Databases ◽  
Frequent Items ◽  
Increasing Demand ◽  
Quantitative Association Rule

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Their algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. The authors’ schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.

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