scholarly journals Survey on Association Rule Hiding Techniques

2019 ◽  
pp. 300-305
Author(s):  
G. Bhavani ◽  
S. Sivakumari
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preservation ◽  
Sensitive Information ◽  
Privacy Preserving Data Mining ◽  
Interesting Part ◽  
Future Improvement ◽  
Sensitive Knowledge

Data mining process extracts useful information from a large amount of data. The most interesting part of data mining is discovering the unseen patterns without unpacking sensitive knowledge. Privacy Preserving Data Mining abbreviated as PPDM deals with the issue of sustaining the privacy of information. This methodology covers the sensitive information from disclosure. PPDM techniques are established for hiding the sensitive information even after performing the data mining. One of the practices to hide the sensitive association rules is termed as association rule hiding. The main objective of association rule hiding algorithm is to slightly adjust the original database so that no sensitive association rule is derived from it. The following article presents a detailed survey of various association rule hiding techniques for preserving privacy in data mining. At first, different techniques developed by previous researchers are studied in detail. Then, a comparative analysis is carried out to know the limitations of each technique and then providing a suggestion for future improvement in association rule hiding for privacy preservation.

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.

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.

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 Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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.

Collusion-Free Privacy Preserving Data Mining

2010 ◽  
Vol 6 (4) ◽  
pp. 30-45 ◽  
Author(s):  
M. Rajalakshmi ◽  
T. Purusothaman ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

Strategies for Sensitive Association Rule Hiding

2013 ◽  
Vol 336-338 ◽  
pp. 2203-2206
Author(s):  
Hui Wang
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preserving Data Mining ◽  
Left Hand ◽  
Popular Knowledge ◽  
Specific Association ◽  
The Right ◽  
Quality Measurements

Data mining technologies are used widely while the side effects it incurred are concerned so seriously. Privacy preserving data mining is so important for data and knowledge security during data mining applications. Association rule extracted from data mining is one kind of the most popular knowledge. It is challenging to hide sensitive association rules extracted by data mining process and make less affection on non-sensitive rules and the original database. In this work, we focus on specific association rule automatic hiding. Novel strategies are proposed which are based on increasing the support of the left hand and decreasing the support of the right hand. Quality measurements for sensitive association rules hiding are presented.

scholarly journals A review on Privacy Preservation and Collaborative Data Mining

2015 ◽  
Vol 14 (12) ◽  
pp. 6368-6372
Author(s):  
Amit Kumar
Keyword(s):  
Data Mining ◽  
Data Transmission ◽  
Privacy Preservation ◽  
Current Data ◽  
Sensitive Information ◽  
Data Mining Technique ◽  
Privacy Preserving Data Mining ◽  
Mining Technique ◽  
Cloud Network ◽  
Collaborative Data Mining

Privacy preservation is major issue in current data transmission over internet and cloud network. For the integrity and security of data various methods are used such as cryptography, data transformation, Steganography, watermarking and many more method. In consequence of all these method some data mining technique is used. The data mining technique provide Varity of algorithm for privacy preservation. The collaborative data mining technique used different agent method for the integrity of security of data during transmission. Issues about privacy-preserving data mining have emerged globally, but still the main problem is that non- sensitive information or unclassified data, one is able to infer sensitive information that is not supposed to be disclosed. Data collection is a necessary step in data mining process. Due to privacy reasons, collecting data from different parries becomes difficult. In this paper presents the review of privacy persevering technique used data mining.

scholarly journals Reinforced Social Ant with Discrete Swarm Optimizer for Sensitive Item and Rule Hiding

2019 ◽  
Vol 8 (9) ◽  
pp. 902-908
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Distributed Databases ◽  
Data Publishing ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Swarm Optimization ◽  
Privacy Preserving Data Mining ◽  
The Past ◽  
Research Areas

In data mining Privacy Preserving Data mining (PPDM) of the important research areas concentrated in recent years which ensures ensuring sensitive information and rule not being revealed. Several methods and techniques were proposed to hide sensitive information and rule in databases. In the past, perturbation-based PPDM was developed to preserve privacy before use and secure mining of association rules were performed in horizontally distributed databases. This paper presents an integrated model for solving the multi-objective factors, data and rule hiding through reinforcement and discrete optimization for data publishing. This is denoted as an integrated Reinforced Social Ant and Discrete Swarm Optimization (RSADSO) model. In RSA-DSO model, both Reinforced Social Ant and Discrete Swarm Optimization perform with the same particles. To start with, sensitive data item hiding is performed through Reinforced Social Ant model. Followed by this performance, sensitive rules are identified and further hidden for data publishing using Discrete Swarm Optimization model. In order to evaluate the RSA-DSO model, it was tested on benchmark dataset. The results show that RSA-DSO model is more efficient in improving the privacy preservation accuracy with minimal time for optimal hiding and also optimizing the generation of sensitive rules.

scholarly journals DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

2019 ◽  
Vol 35 (4) ◽  
pp. 337-354
Author(s):  
Bac Le ◽  
Lien Kieu ◽  
Dat Tran
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Association Rule ◽  
Heuristic Method ◽  
Sensitive Information ◽  
Data Loss ◽  
Individual Privacy ◽  
Maximal Frequent Itemsets ◽  
Sensitive Knowledge ◽  
Privacy Issues

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss.

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