Heterogeneous differential privacy for vertically partitioned databases

Xia, Y; Zhu, T; Ding, X; Jin, H; Zou, D

Heterogeneous differential privacy for vertically partitioned databases

Xia, Y Zhu, T

Ding, X Jin, H Zou, D

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Concurrency and Computation: Practice and Experience, 2020
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Xia, Y
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Ding, X
dc.contributor.author	Jin, H
dc.contributor.author	Zou, D
dc.date.accessioned	2021-01-15T05:02:24Z
dc.date.available	2021-01-15T05:02:24Z
dc.date.issued	2020
dc.identifier.citation	Concurrency and Computation: Practice and Experience, 2020
dc.identifier.issn	1532-0626
dc.identifier.issn	1532-0634
dc.identifier.uri	http://hdl.handle.net/10453/145450
dc.description.abstract	© 2019 John Wiley & Sons, Ltd. Existing privacy-preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information-based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Concurrency and Computation: Practice and Experience
dc.relation.isbasedon	10.1002/cpe.5607
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	Heterogeneous differential privacy for vertically partitioned databases
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-15T05:02:11Z
pubs.publication-status	Published

Abstract:

© 2019 John Wiley & Sons, Ltd. Existing privacy-preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information-based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.

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http://hdl.handle.net/10453/145450