Security of Database for Data Miner with Help of Perturbed Technique
Prasannta Tiwari, Hitesh Gupta · 2014
Today data holder want to utilize and release data to third party for analyzing or researching however, but they not required to disclosing any individual data within its privacy interval to anyone. Here find the answer to what extent confidential information in a perturbed database can be compromised by attackers or snoopers. key of element is preserving privacy and confidentiality of data is the ability to evaluate the extent of all potential disclosure for the data. Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the important data by randomly modifying the important data values using some additive noise and aim to reconstruct the original distribution closely at an aggregate level. The main contribution of this paper lies in the algorithm to accurately reconstruct the community joint density given the perturbed multidimensional stream of data information. Any statistical question about the community can be answered using the reconstructed joint density. In our research objective is to determine whether the distributions of the original and recovered of important data are close enough to each other despite the nature of the noise applied. We are considering an ensemble clustering method to reconstruct the initial data distribution.