Constructing dummy query sequences to protect location privacy and query privacy in location-based services

Wu, Z; Li, G; Shen, S; Lian, X; Chen, E; Xu, G

Constructing dummy query sequences to protect location privacy and query privacy in location-based services

Wu, Z Li, G Shen, S Lian, X Chen, E Xu, G

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: World Wide Web, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Wu, Z
dc.contributor.author	Li, G
dc.contributor.author	Shen, S
dc.contributor.author	Lian, X
dc.contributor.author	Chen, E
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2020-09-30T11:24:19Z
dc.date.available	2020-09-30T11:24:19Z
dc.date.issued	2020-01-01
dc.identifier.citation	World Wide Web, 2020
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/142939
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-020-00830-x
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [World Wide Web, 2020]. The final authenticated version is available online at: [ doi.org/10.1007/s11280-020-00830-x]”
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in World Wide Web. The final authenticated version is available online at: https://dx.doi.org/10.1007/s11280-020-00830-x.	en_US
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Constructing dummy query sequences to protect location privacy and query privacy in location-based services
dc.type	Journal Article
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-09-30T11:24:11Z
pubs.publication-status	Published

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.

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http://hdl.handle.net/10453/142939