Journal ArticleDOI
A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms
TLDR
A new multi-objective method for hiding sensitive association rules based on the concept of genetic algorithms is introduced, fully supporting security of database and keeping the utility and certainty of mined rules at highest level.Abstract:
Extracting of knowledge form large amount of data is an important issue in data mining systems. One of most important activities in data mining is association rule mining and the new head for data mining research area is privacy of mining. Today association rule mining has been a hot research topic in Data Mining and security area. A lot of research has done in this area but most of them focused on perturbation of original database heuristically. Therefore the final accuracy of released database falls down intensely. In addition to accuracy of database the main aspect of security in this area is privacy of database that is not warranted in most heuristic approaches, perfectly. In this paper we introduce new multi-objective method for hiding sensitive association rules based on the concept of genetic algorithms. The main purpose of this method is fully supporting security of database and keeping the utility and certainty of mined rules at highest level.read more
Citations
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Journal ArticleDOI
A comprehensive review on privacy preserving data mining
Yousra Abdul Alsahib S. Aldeen,Yousra Abdul Alsahib S. Aldeen,Mazleena Salleh,Mohammad Abdur Razzaque +3 more
TL;DR: A panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories is provided, which reveals the past development, present research challenges, future trends, the gaps and weaknesses.
Journal ArticleDOI
The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion
TL;DR: A GA-based framework with two optimization algorithms is proposed for data sanitization of PPDM and a novel evaluation function with three concerned factors is designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets.
Journal ArticleDOI
Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms.
TL;DR: A compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is proposed, which solves the limitations of the evolutionary process by adopting both the compact GA- based (cGA) mechanism and the prelarge concept.
Journal ArticleDOI
Association rule hiding using cuckoo optimization algorithm
TL;DR: A new and efficient approach has been introduced which benefits from the cuckoo optimization algorithm for the sensitive association rules hiding (COA4ARH) and the results indicate that this algorithm has superior performance compared to other algorithms.
Journal ArticleDOI
A Survey on Privacy Preserving Association Rule Mining
TL;DR: A review of the state-of-the-art methods for privacy preservation is presented and analyzes the techniques for privacy preserving association rule mining and points out their merits and demerits.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Journal ArticleDOI
Association rule hiding
TL;DR: This work investigates confidentiality issues of a broad category of rules, the association rules, and presents three strategies and five algorithms for hiding a group of associationrules, which is characterized as sensitive.
Proceedings ArticleDOI
Disclosure limitation of sensitive rules
TL;DR: This paper attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemets.
Journal ArticleDOI
Using unknowns to prevent discovery of association rules
TL;DR: This work introduces a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications.