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Outsourced Association Rule Mining For Partitioned Database Using Encryption


Mr. Rajesh R. Kshirsagar , Computer Science and Technology, Department of Technology, Shivaji University Kolhapur, India; Mr.H.A.Tirmare, Computer Science and Technology, Department of Technology, Shivaji University, Kolhapur, India


Association Rule Mining, Frequent Itemset Mining, Privacy-Preserving Data Mining, Partitioned Data


Data analysis techniques that is Association rule mining and Frequent item set mining are two widely used for various applications. The traditional system focused separately on vertically partitioned database and horizontally partitioned databases on the basis of this introducing a system which focus on both horizontally and vertically partitioned databases collaboratively with privacy-preserving mechanism. Data owners need to know the frequent item sets or association rules from a collective data set and disclose or reveal as few information about their unprocessed data as possible to other data owners and third parties. To guarantee data privacy a Symmetric Encryption Technique is used to get better result. Cloud aided frequent item set mining solution used to demonstrate an association rule mining solution. The resultant solutions are designed for outsourced databases that permit various data owners to efficiently share their data securely without compromising on data privacy. Data security is one of the key processes in outsourcing data to various outside users. Traditionally Fast Distribution Mining algorithm was proposed for securing distributed data. This work addresses a problem by secure association rules over partitioned data in both horizontal and vertical. A Frequent item sets algorithm and Distributed association rule mining algorithm is utilized for doing above procedure effectively in partitioned data, which includes services of the data in outsourcing process for distributed databases. This work maintains efficient security over vertical and horizontal view of representation in secure mining applications.

Other Details

Paper ID: IJSRDV5I50853
Published in: Volume : 5, Issue : 5
Publication Date: 01/08/2017
Page(s): 711-716

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