A distortion based technique for preserving privacy in OLAP data cube

This paper is about privacy preservation of the data in OLAP data cube. Data cube is a multidimensional view of a database, which helps in analysis of data from different perspectives. Preserving privacy of individual's data while providing all data available for the analysis is one of the main challenges for OLAP systems. Because legitimate queries on aggregated values lead to the inference of individual's data or sensitive data. We propose a data perturbation technique called uniformly adjusted distortion, which initially distorts one cell and then uniformly distributes this distortion in the whole data cube. This uniform distribution not only preserves the aggregates but also provides maximum accuracy with range sum queries and high availability.