Differentially private model publishing in cyber physical systems

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

Differentially private model publishing in cyber physical systems

Zhu, T Xiong, P Li, G Zhou, W

Yu, PS

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2018
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhu, T	en_US
dc.contributor.author	Xiong, P	en_US
dc.contributor.author	Li, G	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.contributor.author	Yu, PS	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2018	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136896
dc.description.abstract	© 2018 Elsevier B.V. With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2018.04.016	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Distributed Computing	en_US
dc.title	Differentially private model publishing in cyber physical systems	en_US
dc.type	Journal Article
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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.publication-status	Published	en_US

Abstract:

© 2018 Elsevier B.V. With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.

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

http://hdl.handle.net/10453/136896