Quantifying Location Privacy Risks Under Heterogeneous Correlations
Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9343824/ |
| _version_ | 1818921878761242624 |
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| author | Bing Li Hong Zhu Meiyi Xie |
| author_facet | Bing Li Hong Zhu Meiyi Xie |
| author_sort | Bing Li |
| collection | DOAJ |
| description | Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e.g., spatial cloaking, dummy location generation, query caching, and perturbation. However, these LPPMs are vulnerable to inference attacks because of the incompleteness of the captured privacy risks caused by heterogeneous correlations in location data, e.g., semantical, temporal, and social correlations. Consequently, they cannot provide sufficient privacy guarantees due to the absence of embedded heterogeneous correlations in the design process of LPPM. To address these issues, we present QUAD, a framework for quantifying location privacy risks under heterogeneous correlations. QUAD has three features: 1) it enables the modeling and seamless fusion of multiple kinds of correlations that are available to adversaries; 2) it provides a probabilistic representation of the privacy risks faced under heterogeneous correlations; and 3) it achieves the quantification of privacy risks for multiple kinds of LPPMs that are widely used in the literature. To mitigate privacy threats, we propose a defense mechanism embedded with the quantified privacy risks. Extensive experiments on two real-world datasets confirm that QUAD can capture more privacy risks than competitors, and the risks can be dramatically reduced by our defense mechanism. |
| first_indexed | 2024-12-20T01:44:39Z |
| format | Article |
| id | doaj.art-3c5947619a824903b41a71100b63cadd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:44:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3c5947619a824903b41a71100b63cadd2022-12-21T19:57:47ZengIEEEIEEE Access2169-35362021-01-019238762389310.1109/ACCESS.2021.30561529343824Quantifying Location Privacy Risks Under Heterogeneous CorrelationsBing Li0https://orcid.org/0000-0003-4506-1643Hong Zhu1https://orcid.org/0000-0001-9815-3934Meiyi Xie2https://orcid.org/0000-0002-1973-7470School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaCurrently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e.g., spatial cloaking, dummy location generation, query caching, and perturbation. However, these LPPMs are vulnerable to inference attacks because of the incompleteness of the captured privacy risks caused by heterogeneous correlations in location data, e.g., semantical, temporal, and social correlations. Consequently, they cannot provide sufficient privacy guarantees due to the absence of embedded heterogeneous correlations in the design process of LPPM. To address these issues, we present QUAD, a framework for quantifying location privacy risks under heterogeneous correlations. QUAD has three features: 1) it enables the modeling and seamless fusion of multiple kinds of correlations that are available to adversaries; 2) it provides a probabilistic representation of the privacy risks faced under heterogeneous correlations; and 3) it achieves the quantification of privacy risks for multiple kinds of LPPMs that are widely used in the literature. To mitigate privacy threats, we propose a defense mechanism embedded with the quantified privacy risks. Extensive experiments on two real-world datasets confirm that QUAD can capture more privacy risks than competitors, and the risks can be dramatically reduced by our defense mechanism.https://ieeexplore.ieee.org/document/9343824/Data privacyheterogeneous datalocation-based servicesprivacy protection |
| spellingShingle | Bing Li Hong Zhu Meiyi Xie Quantifying Location Privacy Risks Under Heterogeneous Correlations IEEE Access Data privacy heterogeneous data location-based services privacy protection |
| title | Quantifying Location Privacy Risks Under Heterogeneous Correlations |
| title_full | Quantifying Location Privacy Risks Under Heterogeneous Correlations |
| title_fullStr | Quantifying Location Privacy Risks Under Heterogeneous Correlations |
| title_full_unstemmed | Quantifying Location Privacy Risks Under Heterogeneous Correlations |
| title_short | Quantifying Location Privacy Risks Under Heterogeneous Correlations |
| title_sort | quantifying location privacy risks under heterogeneous correlations |
| topic | Data privacy heterogeneous data location-based services privacy protection |
| url | https://ieeexplore.ieee.org/document/9343824/ |
| work_keys_str_mv | AT bingli quantifyinglocationprivacyrisksunderheterogeneouscorrelations AT hongzhu quantifyinglocationprivacyrisksunderheterogeneouscorrelations AT meiyixie quantifyinglocationprivacyrisksunderheterogeneouscorrelations |