Alfredo Cuzzocrea
ABSTRACT
While several research efforts have been developed in the context of privacy-preserving big data management and analytics re- cently, relevant challenges arise when such models, techniques and algorithms must be delivered on top of massive, distributed big data repositories. This problem opens the door to the design of innovative models, techniques and algorithms that, contrary to actual proposals, are able to inject the scalability feature during the privacy-preserving big data management and analytics phase. On the basis of these considerations, this paper provides an overview on actual problems and limitations of state-of-the-art techniques, along with the proposal of an effective framework for supporting scalable privacy-preserving big data management and analytics.
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Index Terms
- Scalable Privacy-Preserving Big Data Management and Analytics: Where We Are and Where We Are Going
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