Additive Gaussian Noise Based Data Perturbation in Multi-Level Trust Privacy Preserving Data Mining

Abstract

Data perturbation is one of the most popular models used in privacy preserving data mining. It is specially convenient for applications where the data owners need to export/publish the privacy-sensitive data. This work proposes that an Additive Perturbation based Privacy Preserving Data Mining (PPDM) to deal with the problem of increasing accurate models about all data without knowing exact details of individual values. To Preserve Privacy, the approach establishes Random Perturbation to individual values before data are published. In Proposed system the PPDM approach introduces Multilevel Trust (MLT) on data miners. Here different perturbed copies of the similar data are available to the data miner at different trust levels and may mingle these copies to jointly gather extra information about original data and release the data is called diversity attack. To prevent this attack MLT-PPDM approach is used along with the addition of random Gaussian noise and the noise is properly correlated to the original data, so the data miners cannot get diversity gain in their combined reconstruction.