Biblio

This paper introduces a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings. The proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the "pedigree" of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so-called Data-dRIven aggregate-PROvenance privacypreserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest.