Analytical Representation on Secure Mining in Horizontally Distributed Database
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 5
Abstract
The security of the large database becomes a serious issue while sharing the data to the network against unauthorized access. However in order to provide the security many researchers cited the issue of Secured Multiparty Computation (SMC) i.e. Secure Third party that allows multiple parties to compute some function of their inputs without disclosing the actual input to one another. Secure sum computation method is popularly and widely accepted due to its simple and thorough solution. The outcomes of our proposed procedure provide a significant result so that it becomes impossible for semi honest party to know the private data of some other sites. Association rule mining is one of the Data Mining techniques used in distributed database. In distributed database the data may be partitioned into fragments and each fragment is assigned to one site. The issue of privacy arises when the data is distributed among multiple sites and no other party wishes to provide their private data to their sites but their main goal is to know the global result obtained by the mining process. However privacy preserving data mining came into the picture. As the database is distributed, different users can access it without interfering with one another. In distributed environment, database is partitioned into disjoint fragments and each site consists of only one fragment. Data can be partitioned in three different ways, that is, horizontal partitioning, vertical partitioning and mixed partitioning. Here we are using horizontal partitioning which is nothing but the data can be partitioned horizontally where each fragment consists of a subset of the records of relation R. Horizontal partitioning divides a table into several tables. The advantage of using horizontal partitioning is, the fact table is partitioned on the basis of time period. Here each time period represents a significant retention period within the business. For example, if the user queries for month to date data then it is appropriate to partition the data into monthly segments. We can reuse the partitioned tables by removing the data in them.
Authors and Affiliations
Raunak Rathi, Prof. A. V. Deorankar
Keywords
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