Hiding association rules by using confidence and support (original) (raw)
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Knowledge and …, 2004
Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms, have increased the disclosure risks that one may encounter when releasing data to outside parties.
Study of Hiding Sensitive Data in Data Mining Using Association Rules
This paper describes Apriori algorithm for association rules for hiding sensitive data in data mining if Large data contain sensitive information that data must be protected from the unauthorized users. Here, we are going to hide this sensitive information in data mining using association rules, when we are going to apply rules for data that time it will falsely hidden information and fake rules falsely generated. So here, we examine confidentiality issues of a broad category of rules, which are called association rules. If the disclosure risk of some of these rules are above a certain privacy threshold, those rules must be characterized as sensitive information in some cases sensitive rules should not be disclose to the public since, other things, they may be used for inference of sensitive data, or they may provided these sensitive data to business competitors with an advantages.
A survey on association rule hiding methods
Rapid growth of information technology has led to creation of huge volumes of data which will be useless if they are not efficiently analyzed. Therefore, various techniques have been provided for retrieving valuable information from huge amounts of data, one of the most common of which is mining association rules. As much as data mining can be important for extracting hidden knowledge from data, it can also reveal sensitive information, which has created some concerns for data owners. Thus, the issue of hiding sensitive knowledge and preserving privacy was raised in data mining. In this paper, different methods for preserving privacy was studied and by mentioning advantages and disadvantages of each method, a suitable platform was provided for researchers to be able to implement the best technique for sanitizing the considered database.
Hiding informative association rule sets
Expert Systems with Applications, 2007
Privacy-preserving data mining, is a novel research direction in data mining and statistical databases, where data mining algorithms are analyzed for the side effects they incur in data privacy [
Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges
International Journal of Computer Applications, 2016
Association rule mining is one of the most used techniques of data mining that are utilized to extract the association rules from large databases. Association rules are one of the most significant assets of any organization that can be used for business growth and profitability increase. It contains sensitive information that threatens the privacy of its publication and it should be hidden before publishing the database. Privacy preserving data mining (PPDM) techniques is used to preserve such confidential information or restrictive patterns from unauthorized access. The pattern can be represented in the form of a frequent itemset or association rule. Also, a rule or pattern is marked as sensitive if its disclosure risk is above a given threshold. This paper discusses the current techniques and challenges of privacy preserving in association rule mining. Also, presentation of metrics used to evaluate the performance of those approaches is also given. Finally, Interesting future trends in this research body are specified.
A Survey on Association Rule Hiding Approaches
— In recent years, explosive growth of the amount of data gathered by transactional systems, a challenge for finding new techniques to extract useful patterns from such a huge amount of data arose. As the database is growing day by day the organizations which maintain this database are worried about the importance of such huge transaction database. One of the greatest challenging tasks of data mining is finding hidden patterns without revealing sensitive information. Privacy preserving data mining (PPDM) is the recent research area that deals with the problem of hiding the sensitive information while analyzing data. PPDM algorithms are evolved for modifying the original data in such that the no sensitive information is revealed even after mining procedure. Association rule hiding is one of the privacy preservation techniques to hide sensitive association rules. All association rule hiding algorithms focus to minimally modify the original database such that no sensitive association rule is derived from it. This paper contains the comprehensive survey of privacy preserving data mining methods. Advantages and disadvantages of the existing algorithms are discussed in brief.
2014
Data mining is the useful technology to extract information or knowledge from large database. However, misuse of this technology may lead to the disclosure of sensitive information. Privacy preserving data mining (PPDM) is new research direction for disclosure of sensitive knowledge. There are various techniques used in PPDM to hide association rules generated by association rule generation algorithms. Main goal of privacy preserving data mining is to find association rules and to hide sensitive association rules. Association rule hiding is the process of modifying original database in such way that sensitive rules are disappeared. In this paper, a survey of various approaches of association rule hiding has been described.
A Survey on Various Methodologies of Hiding Association Rules for Privacy Preserving
Data mining is the useful technology to extract information or knowledge from large database. However, misuse of this technology may lead to the disclosure of sensitive information. Privacy preserving data mining (PPDM) is new research direction for disclosure of sensitive knowledge. There are various techniques used in PPDM to hide association rules generated by association rule generation algorithms. Main goal of privacy preserving data mining is to find association rules and to hide sensitive association rules. Association rule hiding is the process of modifying original database in such way that sensitive rules are disappeared. In this paper, a survey of various approaches of association rule hiding has been described.