Effectiveness of Multiplicative Data Perturbation for Privacy Preserving Data Mining

Abstract

Privacy concerns over the ever-increasing gathering of personal information by various institutions led to the development of privacy preserving data. The approach protects the privacy of the data by perturbing the data through a method. The major challenge of data perturbation is to achieve the desired result between the level of data privacy and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving of data for applications and mining systems. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired level of data utility by selectively preserving the mining task and model specific information during the data perturbation process. The multiplicative perturbation algorithm may find multiple data transformations that preserve the required data utility. Thus the next major challenge is to find a good transformation that provides a satisfactory level of privacy data. we are going to handle the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterns and trends from the original database. I am trying to get advantage of both the dimension like more protected data and relative fast.

Keywords: privacy preservation, multiplicative data perturbation