RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks

The boom of mobile devices and location-based services (LBSs) greatly enriches the mobile social network (MSN) applications, which bring convenience to our daily life and, meanwhile, raise serious privacy concerns due to the potential disclosure risk of location privacy. Besides the single-location...

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Autores principales:	Jinquan Zhang, Xiao Wang, Yanfeng Yuan, Lina Ni
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Privacy preservation mobile social networks trajectory privacy location-based service Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cfb7d0c475e14e1e92cc131bde246fc8
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id	oai:doaj.org-article:cfb7d0c475e14e1e92cc131bde246fc8
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spelling	oai:doaj.org-article:cfb7d0c475e14e1e92cc131bde246fc82021-11-19T00:03:09ZRcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks2169-353610.1109/ACCESS.2019.2927140https://doaj.org/article/cfb7d0c475e14e1e92cc131bde246fc82019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8755993/https://doaj.org/toc/2169-3536The boom of mobile devices and location-based services (LBSs) greatly enriches the mobile social network (MSN) applications, which bring convenience to our daily life and, meanwhile, raise serious privacy concerns due to the potential disclosure risk of location privacy. Besides the single-location privacy, trajectory privacy is another important type for location privacy leakage. In this paper, focusing on the trajectory privacy preservation in MSNs, we propose a privacy preservation scheme based on the radius-constrained dummy trajectory (RcDT) in MSNs. Particularly, by constraining the generated circular range with radius <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> for the location where a user sends LBS requests, we present the radius-constrained dummy location (RcDL) algorithm to generate the dummy location set of the user’s real location. Furthermore, based on the generated dummy locations, we put forward the RcDT algorithm to generate the dummy trajectory set that has higher similarity to the real trajectory comprehensively considering the constraints of both the single-location exposure risk and trajectory exposure risk. Thus, the user’s trajectory privacy preservation in MSNs is enhanced since the possibility of identifying users’ real trajectories and malicious attacks are reduced. The simulation results demonstrate that our RcDT scheme can have better performance and privacy degree than the existing methods.Jinquan ZhangXiao WangYanfeng YuanLina NiIEEEarticlePrivacy preservationmobile social networkstrajectory privacylocation-based serviceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 90476-90486 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Privacy preservation mobile social networks trajectory privacy location-based service Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Privacy preservation mobile social networks trajectory privacy location-based service Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jinquan Zhang Xiao Wang Yanfeng Yuan Lina Ni RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
description	The boom of mobile devices and location-based services (LBSs) greatly enriches the mobile social network (MSN) applications, which bring convenience to our daily life and, meanwhile, raise serious privacy concerns due to the potential disclosure risk of location privacy. Besides the single-location privacy, trajectory privacy is another important type for location privacy leakage. In this paper, focusing on the trajectory privacy preservation in MSNs, we propose a privacy preservation scheme based on the radius-constrained dummy trajectory (RcDT) in MSNs. Particularly, by constraining the generated circular range with radius <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> for the location where a user sends LBS requests, we present the radius-constrained dummy location (RcDL) algorithm to generate the dummy location set of the user’s real location. Furthermore, based on the generated dummy locations, we put forward the RcDT algorithm to generate the dummy trajectory set that has higher similarity to the real trajectory comprehensively considering the constraints of both the single-location exposure risk and trajectory exposure risk. Thus, the user’s trajectory privacy preservation in MSNs is enhanced since the possibility of identifying users’ real trajectories and malicious attacks are reduced. The simulation results demonstrate that our RcDT scheme can have better performance and privacy degree than the existing methods.
format	article
author	Jinquan Zhang Xiao Wang Yanfeng Yuan Lina Ni
author_facet	Jinquan Zhang Xiao Wang Yanfeng Yuan Lina Ni
author_sort	Jinquan Zhang
title	RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
title_short	RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
title_full	RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
title_fullStr	RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
title_full_unstemmed	RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks
title_sort	rcdt: privacy preservation based on r-constrained dummy trajectory in mobile social networks
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/cfb7d0c475e14e1e92cc131bde246fc8
work_keys_str_mv	AT jinquanzhang rcdtprivacypreservationbasedonrconstraineddummytrajectoryinmobilesocialnetworks AT xiaowang rcdtprivacypreservationbasedonrconstraineddummytrajectoryinmobilesocialnetworks AT yanfengyuan rcdtprivacypreservationbasedonrconstraineddummytrajectoryinmobilesocialnetworks AT linani rcdtprivacypreservationbasedonrconstraineddummytrajectoryinmobilesocialnetworks
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RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks

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