A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAP

This paper complements the

privacy preserving distributed

OLAP

framework

proposed by us in a previous work by introducing

four major theoretical properties

that extend models and algorithms presented in the previous work, where the experimental validation of the framework has also been reported. Particularly, our framework makes use of the

CUR

matrix decomposition technique

as the elementary component for

computing privacy preserving two-dimensional

OLAP

views effectively and efficiently

. Here, we investigate theoretical properties of the

CUR

decomposition method, and identify four theoretical extensions of this method, which, according to our vision, may result in benefits for a wide spectrum of aspects in the context of privacy preserving distributed

OLAP

, such as

privacy preserving knowledge fruition schemes and query optimization

. In addition to this, we also provide a widespread experimental analysis of the framework, which fully confirms to us the major practical achievements, in terms of both efficacy and efficiency, due to our framework.