Authors:
Alfredo Cuzzocrea
1
;
2
and
Selim Soufargi
1
Affiliations:
1
iDEA Lab, University of Calabria, Rende, Italy
;
2
Department of Computer Science, University of Paris City, Paris, France
Keyword(s):
Big Data, Privacy-Preserving Big Data, Big Hierarchical Data, Co-Occurrence Analysis, Multidimensional Big Data Analytics, Privacy-Preserving Multidimensional Big Data Analytics.
Abstract:
Nowadays, Big Data Analytics is gaining the momentum in both the academic and industrial research communities. In this context, the issue of performing such a critical process under tight privacy-preservation constraints plays the critical role of “enabling technology”. This paper, by perfectly aligning with the depicted paradigm, introduces and experimentally assesses Drill-CODA, an innovative framework that combines drill-across multidimensional big data analytics and co-occurrence analysis to finally achieve privacy-preservation during the analytical phase.