Making the pedigree to your big data repository: Innovative methods, solutions, and algorithms for supporting big data privacy in distributed settings via data-driven paradigms

Alfredo Cuzzocrea, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Starting from our previous research where we introduced a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings, in this paper we further and significantly extend our past research contributions, and provide several novel contributions that complement our previous work in the investigated research field. Our 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 privacy-preserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest. Extensions and discussion on main motivations and principles of our proposed research, two relevant case studies that clearly state the need-for and covered (related) properties of supporting privacy-preserving management and analytics of big data in modern distributed systems, and an experimental assessment and analysis of our proposed DRIPROM framework are the major results of this paper.

Original languageBritish English
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
PublisherIEEE Computer Society
Pages508-516
Number of pages9
ISBN (Electronic)9781728126074
DOIs
StatePublished - Jul 2019
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: 15 Jul 201919 Jul 2019

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2
ISSN (Print)0730-3157

Conference

Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/TerritoryUnited States
CityMilwaukee
Period15/07/1919/07/19

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