A Survey: Preventing Discovering Association Rules for Large Data Base

M. Patel¹ , A. Hasan² , S.Kumar ³

Section:Review Paper, Product Type: Isroset-Journal
Vol.1 , Issue.2 , pp.30-32, Mar-2013

Online published on Apr 30, 2013

Copyright © M. Patel, A. Hasan , S.Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data products are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Whether data is personal or corporate data, data mining offers the potential to reveal what other regard as sensitive (private). In some cases, it may be of mutual benefit for two parties (even competitors) to share their data for an analysis task. Sensitive knowledge which can be mined from a database by using data mining algorithms should also be excluded, because such knowledge can equally well compromise data privacy, as we will indicate. The main objective in privacy preserving data mining is to develop algorithms for modifying the original data in some way, so that the private data and private knowledge remain private even after the mining process. The problem that arises when confidential information can be derived from released data by unauthorized users is also commonly called the “database inference” problem.

Key-Words / Index Term :
Association Rule mining, Data mining

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