An Enhanced Utility Mining Algorithm for Hiding Frequent Item sets

Selvaraj Rajalakshmi

Department of Computing,

BIUST, Botswana

Abstract

Data mining techniques and algorithms plays a vital role in knowledge discovery. Data miners often need full access to the data in order to build accurate models. Data mining, with its promise to efficiently discover valuable, non-obvious information from large databases, is particularly vulnerable to misuse. Data mining is performed with large database, where it contains sensitive information. Providing security to sensitive data against unauthorized access has been a long term goal for the database security research community and for the government statistical agencies. Hence, the security issue has become, recently, a much more important area of research in data mining. However, one of the biggest barriers facing data mining projects today is the “inability to release data” due to privacy concerns.

The concept of privacy-preserving data mining has recently been proposed in response to the above concerns. Privacy is a term which is associated with the mining task so that we are able to hide some crucial information which we don’t want to disclose it to the public. So the concept privacy preserving data mining is the process of preserving personal information from data mining algorithms. There are two types of privacy in data mining. The first type of privacy is output privacy; here the data is minimally altered so that the mining result will preserve certain privacy. The second type of privacy is called input privacy, in which the data is manipulated so that mining result is not affected or minimally affected. To solve this problem here we propose a new Modified Privacy Preserving Utility Mining algorithm to hide the sensitive item sets.

Introduction

Privacy issues are further exacerbated by the internet which makes it easy for the new data to be automatically collected and added to databases . The goal of privacy preserving data mining is to develop algorithms for modifying the original data in some way, so that the private data and knowledge remain confidential even after the mining process [8]. The main consideration in privacy preserving data mining is twofold. First, sensitive raw data like identifiers, names, addresses and more, should be modified or trimmed out from the original database. Second, sensitive knowledge that are mined from a database.

 

 

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Volume -01, Issue 05, December 2013

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