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Privacy Preserving Secured Mining of Heterogeneous Data with Attack Prohibition System

 

Privacy Preserving Secured Mining of Heterogeneous Data with Attack Prohibition System

S.Sindhu Biravi, M.Kalaiselvi


Abstract:

Spurred by developments such as secured mining, there has been considerable recent interest in paradigm of data mining-as-a-service. The company lacking in expertise or computational resources can outsource it’s data to the server. Transaction Items and association rules of outsourced database are considered private property of data owner. To protect privacy, data owner transforms and ships the data to server, by sending mining queries to server and true patterns can be recovered. In this paper, problem of outsourcing association rule mining task within the corporate privacy-preserving framework is focused. Proposed an attack model based on background knowledge and optimized distributed association rule mining (ODARM) for privacy preserving outsourced mining. ODARM algorithm ensures that each transformed item was indistinguishable, with respect to Attacker’s background knowledge, from at least k-1 other transformed items. Our comprehensive experiments on the very large and real transaction database demonstrate that techniques protect heterogeneous data effectively.

Keywords: Association rule mining, Frequent item set mining, ODARM, Data anonymization.

Volume: 5 | Issue: Special Issue

Pages: 51-60

Paper ID : IJCCTSSP8

Issue Date: Jan - Dec , 2017

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