Towards a theory for privacy preserving distributed OLAP
Privacy Preserving Distributed OLAP identifies a collection of models, methodologies and algorithms devoted to ensuring the privacy of multidimensional OLAP data cubes in distributed environments. While there is noticeable research on practical and pragmatic aspects of Privacy Preserving OLAP, both in centralized and distributed environments, the active literature is lacking of contributions falling in the theory-side of this emerging research topic. Contrary to this, according to our vision, there is a significant need for theoretical results, which may involve in benefits for a wide spectrum of aspects, such as privacy preserving knowledge fruition schemes and query optimization. Inspired by these considerations, starting from our previous research result where the main privacy preserving distributed OLAP framework has been introduced, this paper proposes some theoretical results that nicely extend the capabilities and the potentialities of the framework above.
Data cubes, OLAP, privacy, distributed, theoretical results
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