balancing accuracy and privacy of olap aggregations on data cubes
In this paper we propose an innovative framework based on flexible sampling-based data cube compression techniques for computing privacy preserving OLAP aggregations on data cubes while allowing approximate answers to be efficiently evaluated over such aggregations . In our proposal, this scenario is accomplished by means of the so-called accuracy/privacy contract , which determines how OLAP aggregations must be accessed throughout balancing accuracy of approximate answers and privacy of sensitive ranges of multidimensional data.
privacy preserving olap