Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.

In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can b...

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Autores principales:	Yunfeng Wang, Mingzhen Li, Yang Xin, Guangcan Yang, Qifeng Tang, Hongliang Zhu, Yixian Yang, Yuling Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/60cdb67ee0d6452dac65513c7262355f
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id	oai:doaj.org-article:60cdb67ee0d6452dac65513c7262355f
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spelling	oai:doaj.org-article:60cdb67ee0d6452dac65513c7262355f2021-12-02T20:14:34ZExchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.1932-620310.1371/journal.pone.0256892https://doaj.org/article/60cdb67ee0d6452dac65513c7262355f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256892https://doaj.org/toc/1932-6203In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility.Yunfeng WangMingzhen LiYang XinGuangcan YangQifeng TangHongliang ZhuYixian YangYuling ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256892 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yunfeng Wang Mingzhen Li Yang Xin Guangcan Yang Qifeng Tang Hongliang Zhu Yixian Yang Yuling Chen Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
description	In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility.
format	article
author	Yunfeng Wang Mingzhen Li Yang Xin Guangcan Yang Qifeng Tang Hongliang Zhu Yixian Yang Yuling Chen
author_facet	Yunfeng Wang Mingzhen Li Yang Xin Guangcan Yang Qifeng Tang Hongliang Zhu Yixian Yang Yuling Chen
author_sort	Yunfeng Wang
title	Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
title_short	Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
title_full	Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
title_fullStr	Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
title_full_unstemmed	Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.
title_sort	exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in location-based social networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/60cdb67ee0d6452dac65513c7262355f
work_keys_str_mv	AT yunfengwang exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT mingzhenli exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT yangxin exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT guangcanyang exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT qifengtang exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT hongliangzhu exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT yixianyang exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks AT yulingchen exchangingregistereduserssubmittingreviewstowardstrajectoryprivacypreservationforreviewservicesinlocationbasedsocialnetworks
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Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.

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