For specific business problems, organizations share data and outsource. Preserving privacy of private data holds a vital role in business analytics. Consulting firms often handle sensitive third party data as part of client projects. By sharing their data, organizations face great risks while most of this sharing takes place with little furtiveness. These process increases the legal responsibility of the parties. So, it is severe to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-theart methods for privacy preservation is presented. A novel perturbation technique using non synthetic additive perturbation technique for association rule mining is proposed in this paper. The above technique minimizes information loss that is common in synthetic perturbed data.
T. Ravi, R. Prasanna Kumar, and Napa, K. Kumar, “A non synthetic data perturbation technique for privacy preservation in association rule mining”, International Journal of Applied Engineering Research, vol. 9, no. 24, pp. 24311-24320, 2014.