Privacy-Preserving OLAP via Modeling and Analysis of Query Workloads: Innovative Theories and Theorems

Abstract

This paper proposes innovative theories and theorems in the context of a state-of-the-art paper that computes privacy-preserving OLAP cubes via modeling and analyzing query workloads. The work contributes to actual literature by devising a solid theoretical framework that can be used for future optimization opportunities.