Pedigree-ing your big data: Data-driven big data privacy in distributed environments

Alfredo Cuzzocrea, Ernesto Damiani

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

9 Scopus citations

Abstract

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.

Original languageBritish English
Title of host publicationProceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages675-681
Number of pages7
ISBN (Electronic)9781538658154
DOIs
StatePublished - 13 Jul 2018
Event18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018 - Washington, United States
Duration: 1 May 20184 May 2018

Publication series

NameProceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018

Conference

Conference18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018
Country/TerritoryUnited States
CityWashington
Period1/05/184/05/18

Keywords

  • Big Data Applications
  • Big Data Privacy
  • Big Data Systems
  • Big Distributed Data
  • Big Multidimensional Data

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