Association Rule for Privacy Preserving in Data Mining (original) (raw)
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Review of Association Rule in Privacy Preserving Data Mining
Data mining is a popular analysis tool to extract information from collection of huge amount of data. The objective of this paper is to review an association rule hiding algorithm for privacy preserving data mining which would be used for providing confidentiality and improve the performance when the database stores and retrieves large amount of data. Association rule hiding which is one of the techniques of PPDM to protect the association rules generated by association rule mining. Association rule hiding refers to the process of changing the original database in such a way that certain sensitive association rules hide without seriously affecting the data and the non-sensitive rules. Association rule mining is an significant data-mining technique that finds riveting association among huge amount of data items.
Survey on Privacy-Preserving Mining of Association Rule and Double Encryption Technique
Data mining can extract important knowledge from large data collections, but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. Such data is available is huge amount so it is very difficult to find out the data and relationship among items. For this problem, association rule mining with improved cryptographic technique is one of the solutions of data mining techniques, which can efficiently correlate the items. The output of such technique can be used in many real time applications to take the proper decisions. But the data owner, who shares their data for mutual advantages, wants to secure their data in association rule mining process. Because it can reveal the sensitive data, which might be harmful. Therefore it becomes very challenging task to achieve the security of data while mining the knowledge from it. This paper represents the core idea of privacy preserving association rule mining on vertically partitioned data with use of improved cryptographic technique.
Privacy Preserving of Association Rule Mining: A Review
2015
when adequate amount of data was available, it was stored in traditional databases, but with the growth in companies and development of internet technologies data keeps on growing and has reached to explosion. So in order to manage them properly we require data mining. Data Mining is extraction of rich knowledge from poor information that we have in database. These databases consist of sensitive and insensitive data or knowledge. Sensitive data are those data which consist of secret and important information and whose owners don’t want it to be leaked. Privacy preservation is also required for secure transformation of personal data. For example, medical information, Insurance policy information, etc... Thus in this paper we have presented different types of association rules to be mined and how different privacy preserving techniques and algorithms for different levels of mining can be applied on them to protect the privacy of data with less information loss and high accuracy. Also ...
This paper presents a performance testing of proposed algorithm. This proposed methodology takes less number of passes than that of the existing algorithm. It means that the time complexity of the proposed method is less as compared to the existing method. In this paper, we also provide the result of algorithm and we are checked the comparison of existing and proposed algorithm. In this paper, we also discussed about the procedure on our algorithm and applying these methodology with number of entries with aspect of execution time and number of modified entries. Privacy preserving in data mining is a very popular research topic. A large number of researchers are working on improving security in data mining. Also a detailed review of the work accomplished in this area is also given along with the coordinates of each work to the classification hierarchy.
A Survey on the Privacy Preserving Algorithm and techniques of Association Rule Mining
In recent years, data mining is a popular analysis tool to extract knowledge from collection of large amount of data. One of the great challenges of data mining is finding hidden patterns without revealing sensitive information. Privacy preservation data mining (PPDM) is answer to such challenges. It is a major research area for protecting sensitive data or knowledge while data mining techniques can still be applied efficiently. Association rule hiding is one of the techniques of PPDM to protect the association rules generated by association rule mining. In this paper, we provide a survey of association rule hiding methods for privacy preservation. Various algorithms have been designed for it in recent years. In this paper, we summarize them and survey current existing techniques for association rule hiding.
A survey on privacy preserving association rule mining
By developing information technology and production methods and collecting data, a great amount of data is daily being collected in commercial, medical databases. Some of this information is important with respect to competition concept in organizations and individual misuses. Nowadays in order to mine knowledge among a great amount of data, data mining tools are used. In order to protect information, fast processing and preventing from revealing private data to keep privacy is presented in data mining. In this article, some techniques in preserving privacy of association rule mining are introduced and some hiding algorithms of association rules are evaluated.
An Association Rule hiding Algorithm for Privacy Preserving Data Mining
International Journal of Control and Automation, 2014
Privacy preserving data mining is a research area concerned with the privacy driven from personally identifiable information when considered for data mining. This paper addresses the privacy problem by considering the privacy and algorithmic requirements simultaneously. The objective of this paper is to implement an association rule hiding algorithm for privacy preserving data mining which would be efficient in providing confidentiality and improve the performance at the time when the database stores and retrieves huge amount of data. This paper compares the performance of proposed algorithm with the two existing algorithms namely ISL and DSR.
International Journal of Computer Applications, 2016
Yesteryears, data mining has emerged as a very popular tool for extracting hidden knowledge from collection of huge amount of data. Major challenges of data mining are to find the hidden knowledge in the data while the sensitive information is not revealed. Many Industry ,Defence ,Public Sector and Organisation facing risk or having security issue while sharing their data so it is very crucial concern how to protect their sensitive information due to legal and customer concern. Many strategies have been proposed to hide the information containing sensitive data. Privacy preserving data mining is an answer to such problems. Association rule hiding is one of the PPDM techniques to protect the sensitive association rule .In this paper, all the approaches for privacy preserving data mining have been compared theoretically and points out their pros and cons.