An Algorithm for Hiding Association Rules on Data Mining (original) (raw)
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Association Rule Hiding for Data Mining
Advances in Database Systems, 2010
Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.
A Method for Hiding Association rules with Minimum Changes in Database
Privacy preserving data mining is a continues way for to use data mining, without disclosing private information. To prevent disclosure of sensitive information by data mining techniques, it is necessary to make changes to the data base. Association rules are important and efficient data mining technique. In order to achieve this algorithm is proposed, that as well as hiding sensitive association rules, having the lowest side effects on the original data set. Proposed algorithm by removing selective item, among items of antecedent sensitive rule (L.H.S.), causes to decrease confidence of sensitive rule below less them threshold and hide the sensitive rule. Also keeps sensitive rules until the end of securing process is reduce the failure hiding, and because the internal clustering, hiding sensitive rules performed synchronic takes insensitive rules to reduce the loss. This algorithm is compared with basic algorithm, on dense and sparse data base. The results with criteria of hiding failure, is indicates 41.6% improvement in dense data base and 28% in made with software data base. With criteria of lost rule, is indicates 70%, 57.1% and 83.3% improvement over the base algorithm. Which indicates the proposed algorithm is efficient.
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.
Survey on Association Rule Hiding Techniques
International Journal of Scientific Research in Science, Engineering and Technology, 2019
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.
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.
Association Rules Hiding for Privacy Preserving Data Mining: A Survey
International Journal of Computer Applications, 2016
(PPDM) privacy preserving data mining is recent advanced research in (DM) data mining field; Many efficient and practical techniques have been proposed for hiding sensitive patterns or information from been discovered by (DM) data mining algorithms. (ARM) Association rule mining is the most important tool in (DM) data mining, that is considered a powerful and interested tool for discovering relationships between items, which are hidden in large database and may provide business competitors with an advantage, thus the hiding of association rules is the most important point in (PPDM) privacy preserving data mining for protecting sensitive and crucial data against unauthorized access; Many Practical techniques and approaches have been proposed for hiding association rules for (PPDM) privacy preserving data mining; In this paper the current existing techniques and algorithms for all approaches for (ARH) association rule hiding have been summarized.
An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining
International Journal, 2011
The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Association analysis is a powerful tool for discovering relationships which are hidden in large database. Association rules hiding algorithms get strong and efficient performance for protecting confidential and crucial data. Data modification and rule hiding is one of the most important approaches for secure data. The objective of the proposed Association rule hiding algorithm for privacy preserving data mining is to hide certain information so that they cannot be discovered through association rule mining algorithm. The main approached of association rule hiding algorithms to hide some generated association rules, by increase or decrease the support or the confidence of the rules. The association rule items whether in Left Hand Side (LHS) or Right Hand Side (RHS) of the generated rule, that cannot be deduced through association rule mining algorithms. The concept of Increase Support of Left Hand Side (ISL) algorithm is decrease the confidence of rule by increase the support value of LHS. It doesn't work for both side of rule; it works only for modification of LHS. In Decrease Support of Right Hand Side (DSR) algorithm, confidence of the rule decrease by decrease the support value of RHS. It works for the modification of RHS. We proposed a new algorithm solves the problem of them. That can increase and decrease the support of the LHS and RHS item of the rule correspondingly so that more rule hide less number of modification. The efficiency of the proposed algorithm is compared with ISL algorithms and DSR algorithms using real databases, on the basis of number of rules hide, CPU time and the number of modifies entries and got better results.
A heuristic algorithm for quick hiding of association rules
Increasing use of data mining process and extracting of association rules caused the introduction of privacy preserving in data mining. A complete publication of the database is inconsistent with security policies and it would result in disclosure of some sensitive data after performing data mining. Individuals and organizations should secure the database before the publication, because if they neglect this issue they will be harmed. The owners of database consider factors such as database size, precision in immunization and velocity in choosing the right approach in order to hide the association rules. Besides the large volume of data and precision in immunization, we should optimize the time of operation and this is one of the issues that has received a little attention. In this paper, FHA algorithm is introduced for hiding sensitive patterns. In this algorithm, it is being tried to reduce the overload of ordering transactions by decreasing database scans. Also, we have reduced the side effects by selecting the appropriate item for performing the modifications. Conducted experiments indicate the execution of this algorithm in appropriate hiding of sensitive association rules.
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.
International Journal of Computer Applications, 2012
Association rule mining is a powerful model of data mining used for finding hidden patterns in large databases. One of the great challenges of data mining is to protect the confidentiality of sensitive patterns when releasing database to third parties. Association rule hiding algorithms sanitize database such that certain sensitive association rules cannot be discovered through association rule mining techniques. In this study, we propose two algorithms, ADSRRC (Advanced Decrease Support of R.H.S. items of Rule Cluster) and RRLR (Remove and Reinsert L.H.S. of Rule), for hiding sensitive association rules. Both algorithms are developed to overcome limitations of existing rule hiding algorithm DSRRC (Decrease Support of R.H.S. items of Rule Cluster). Algorithm ADSRRC overcomes limitation of multiple sorting in database as well as it selects transaction to be modified based on different criteria than DSRRC algorithm. Algorithm RRLR overcomes limitation of hiding rules having multiple R.H.S. items. Experimental results show that both proposed algorithms outperform DSRRC in terms of side effects generated and data quality in most cases.