Title :
Further Theoretical Contributions to a Privacy Preserving Distributed OLAP Framework
Author :
Cuzzocrea, Alfredo ; Bertino, Elisa
Author_Institution :
ICAR, Univ. of Calabria, Rende, Italy
Abstract :
This paper complements our privacy preserving distributed OLAP framework proposed in [8] by introducing four major theoretical properties that extend models and algorithms presented in [8], where the experimental validation of the framework has also been reported. Particularly, the framework [8] makes use of the CUR matrix decomposition technique [12] 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 some 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.
Keywords :
data mining; data privacy; matrix decomposition; CUR matrix decomposition technique; online analytical processing; privacy preserving distributed OLAP framework; privacy preserving knowledge fruition schemes; query optimization; two-dimensional OLAP views; Aggregates; Data privacy; Matrix decomposition; Nickel; Privacy; Random variables; Privacy Preserving Distributed OLAP; Privacy Preserving OLAP; Theory;
Conference_Titel :
Computer Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual
Conference_Location :
Kyoto
DOI :
10.1109/COMPSAC.2013.39