Multilevel Trust in Privacy Preserving Data Mining using Random Rotation Based Data Perturbation

Abstract

Rapid growth of internet technology have made possible to utilize remote communication in every aspects of life. As well as technology is exceeded, the need of privacy and security in electronic communications became hot issues. The main goal of this research is to develop and implement data privacy appropriate technique. Privacy preserving data is to develop methods without increasing the risk of misuse of the data used to generate those methods. A number of effective methods for privacy preserving data mining have been proposed. A previously studied perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, by considering assumption and expanding the scope of perturbation-based PPDM to Multilevel Trust (MLT-PPDM). Results the more trusted a data miner is, the less perturbed copy of the data it can access.