An iteration-based differentially private social network data release

Zhu, T; Yang, M; Xiong, P; Xiang, Y; Zhou, W

An iteration-based differentially private social network data release

Zhu, T

Yang, M Xiong, P Xiang, Y Zhou, W

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Publication Type:: Journal Article
Citation:: Computer Systems Science and Engineering, 2018, 33 (2), pp. 61 - 69
Issue Date:: 2018-03-01

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	AN ITERATION-BASED DIFFERENTIALLY PRIVATE SOCIAL NETWORK DATA RELEASE.pdf	Published Version	724.8 kB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947	en_US
dc.contributor.author	Yang, M	en_US
dc.contributor.author	Xiong, P	en_US
dc.contributor.author	Xiang, Y	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Computer Systems Science and Engineering, 2018, 33 (2), pp. 61 - 69	en_US
dc.identifier.issn	0267-6192	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133425
dc.description.abstract	© 2018 CRL Publishing Ltd. Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users' privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large set of queries are used as update criteria. Compared with existing works, the proposed method enhances the accuracy of query results. The extensive experiment proves that the proposed solution outperforms two state-of-the-art methods, the Laplace method and the correlated method, in terms of Mean Absolute Value. It means our methods can retain more utility of the queries while preserving the privacy.	en_US
dc.relation.ispartof	Computer Systems Science and Engineering	en_US
dc.subject.classification	Information Systems	en_US
dc.title	An iteration-based differentially private social network data release	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	33	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2018 CRL Publishing Ltd. Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users' privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large set of queries are used as update criteria. Compared with existing works, the proposed method enhances the accuracy of query results. The extensive experiment proves that the proposed solution outperforms two state-of-the-art methods, the Laplace method and the correlated method, in terms of Mean Absolute Value. It means our methods can retain more utility of the queries while preserving the privacy.

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

http://hdl.handle.net/10453/133425