Differentially Private Data Publishing and Analysis: A Survey

Zhu, T; Li, G; Zhou, W; Yu, PS

Differentially Private Data Publishing and Analysis: A Survey

Zhu, T

Li, G Zhou, W

Yu, PS

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2017, 29, (8), pp. 1619-1638
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Li, G
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521
dc.contributor.author	Yu, PS
dc.date.accessioned	2022-07-28T03:40:42Z
dc.date.available	2022-07-28T03:40:42Z
dc.date.issued	2017-08-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2017, 29, (8), pp. 1619-1638
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/159281
dc.description.abstract	Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2017.2697856
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Differentially Private Data Publishing and Analysis: A Survey
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-28T03:40:41Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	8

Abstract:

Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/159281