A review of privacy preserving models for multi-party data release framework

Abstract

Nowadays, with the improvement of internet technology and advancement in distributed computing data is increasing rapidly. There is a need of information sharing between organizations. Ideally, we wish to share data from multiple private databases and want to extract information or knowledge from it. For this reason, organizations integrate their data to carry decision making task. The integration should be done in such a way that privacy of individual should not be at risk. There are many techniques such as privacy preserving data mining, k-anonymity, l-diversity and other techniques are developed for preventing the privacy of data owner. In this paper, we present a survey of various privacy preserving models and suggest a new technique for privacy preservation in multi-party data release framework that improving the privacy with minimum cost requirements and suitable for both numerical and categorical sensitive attributes.