DocumentCode :
541811
Title :
Privacy preserving data mining based on association rule- a survey
Author :
Vijayarani, S. ; Tamilarasi, A. ; SeethaLakshmi, R.
Author_Institution :
Sch. of Comput. Sci. & Eng., Bharathiar Univ., Coimbatore, India
fYear :
2010
fDate :
27-29 Dec. 2010
Firstpage :
99
Lastpage :
103
Abstract :
Data mining is the process of extracting hidden information from the database. Data mining is emerging as one of the key features of many business organizations. The current trend in business collaboration shares the data and mined results to gain mutual benefit. The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms to leverage this information. Apart from classification and regression, one of the most important tasks of data mining is to find patterns in data. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in enormous amount of data impose new threats on the seamless integration of information. In this paper, we consider the problem of building privacy preserving algorithms for one category of data mining techniques, the association rule mining.
Keywords :
business data processing; data mining; data privacy; information retrieval; pattern classification; regression analysis; association rule mining; business collaboration; business organization; data pattern; data sharing; hidden information extraction; knowledge discovery; mutual benefit; privacy preserving data mining; Algorithm design and analysis; Association rules; Data privacy; Distributed databases; Itemsets; Association rule; Data Mining; Privacy Preserving Data Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication and Computational Intelligence (INCOCCI), 2010 International Conference on
Conference_Location :
Erode
Electronic_ISBN :
978-81-8371-369-6
Type :
conf
Filename :
5738807
Link To Document :
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