A Constraint-Based Framework for Computing Privacy Preserving OLAP Aggregations on Data Cubes

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Provided by: RWTH Aachen University
Topic: Data Management
Format: PDF
A constraint-based framework for computing privacy preserving OLAP (OnLine Analytical Processing) aggregations on data cubes is proposed and experimentally assessed in this paper. The author's 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. To this end, they devise a threshold-based method that aims at simultaneously accomplishing the so-called privacy constraint, which inferiorly bounds the inference error.
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