Computing privacy preserving OLAP aggregations on data cubes: A constraint-based approach

A constraint-based framework for computing privacy preserving OLAP aggregations on data cubes is proposed and experimentally assessed in this paper. Our framework introduces a novel privacy OLAP notion, which, following consolidated paradigms of OLAP research, looks at the privacy of aggregate patterns defined on multidimensional ranges rather than the privacy of individual tuples/data-cells, like similar efforts in privacy preserving database and data-cube research. We complete our main theoretical contribution by means of an experimental evaluation and analysis of the effectiveness of our proposed framework on synthetic, benchmark and real-life data cubes.