Novel Privacy Preserving Model to Explore Association Rule based Item sets from Outsourced Transactional Data sets

Srihari Varama Mantena, CVPR Prasad

Data outsourcing is the major concept in cloud computing with advance implementation for business intelligence (BI) related information technology (IT) organizations. Real time business intelligence IT, supermarket organizations generates high amount of data in terms of transactions with associated rules from outsourced transactional databases. Data outsourcing is required to store generated data into cloud related applications because of data mining as a service, in this scenario data can share via third party service provider to mine transactional patterns,so there is a necessary to provide privacy for data relates to each user transactions. So that, discuss the problem of outsourcing associated user data in distributed environment. In this paper, propose and introduce Novel Privacy Preserving Attacker Model (NPPAM)to provide privacy based on background knowledge relates to mine transactional item sets from outsourced user transactional data using 1-1 substitution cryptographic method. This approach explores each user transaction is transformed and distinguishes with respect to attackers knowledge from transactional data sources. Experiments of this approach demonstrate efficient and scalable privacy on transactional data.

Volume 12 | Issue 2

Pages: 1031-1037

DOI: 10.5373/JARDCS/V12I2/S20201131