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Big privacy: challenges and opportunities of privacy study in the age of big data

Yu, Shui 2016, Big privacy: challenges and opportunities of privacy study in the age of big data, IEEE access, vol. 4, pp. 2751-2763, doi: 10.1109/ACCESS.2016.2577036.

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Title Big privacy: challenges and opportunities of privacy study in the age of big data
Author(s) Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Journal name IEEE access
Volume number 4
Start page 2751
End page 2763
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016
ISSN 2169-3536
Keyword(s) Big data
privacy
data clustering
differential privacy
Summary One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land.
Language eng
DOI 10.1109/ACCESS.2016.2577036
Field of Research 080109 Pattern Recognition and Data Mining
080303 Computer System Security
080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085797

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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Citation counts: TR Web of Science Citation Count  Cited 3 times in TR Web of Science
Scopus Citation Count Cited 8 times in Scopus
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Created: Wed, 31 Aug 2016, 14:20:48 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.