Protecting Sensitive Data using Multilevel Privacy Preserving Framework Withstanding Linear and Non Linear Attacks

Abstract

Rapid development of data mining technologies has brought a serious threat to the security of private sensitive information. There is unwanted disclosure of private sensitive information during the process of data collection, data publishing and data mining operations. Developing data mining models without accessing private information as well as without compromising the utility of mining result has become a major concern. Various approaches towards Data Perturbation techniques are limited with manipulation of Gaussian noise to the original data. But Gaussian noise is likely prone to linear attacks. In this paper, we study the effect of adding Gaussian and Laplacian noise as a two step process for random data perturbation. We also consider this effect in multiple trust levels, where the data miners receive their perturbed copies only according to their trust levels. We prove that our solution is vigorous against both linear and non linear attacks. Malicious data miner cannot reconstruct the original data by having access to differently perturbed copies of same data. Our solution thus proves that the combination of Gaussian and Laplacian noise can withstand with both linear and non linear attacks.