Dummy Generation Based on User-Movement Estimation for Location Privacy Protection
Location-based services (LBSs) have been becoming more common due to the prevalence of GPS-enabled devices. While LBSs bring many benefits for our daily lives, location information may reveal private information, rendering an important problem of protecting location privacy of users. To anonymize th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8347080/ |
| _version_ | 1818331646012686336 |
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| author | Shuhei Hayashida Daichi Amagata Takahiro Hara Xing Xie |
| author_facet | Shuhei Hayashida Daichi Amagata Takahiro Hara Xing Xie |
| author_sort | Shuhei Hayashida |
| collection | DOAJ |
| description | Location-based services (LBSs) have been becoming more common due to the prevalence of GPS-enabled devices. While LBSs bring many benefits for our daily lives, location information may reveal private information, rendering an important problem of protecting location privacy of users. To anonymize the locations of users, we focus on dummy-based approaches that generate dummies and their locations are sent along with the actual location of a user to an LBS provider. Although several existing studies developed dummy-based techniques, they assume unrealistic user mobility, e.g., users keep moving and do not stop or follow a pre-defined movement plan precisely. In this paper, we remove the unrealistic assumptions and require much easier input with respect to user movement, i.e., only a set of visiting points. Under the assumption, we propose a dummy generation method, estimation-based dummy trajectory generation (Edge). Based on the given visiting points, Edge estimates a user-movement plan and designs trajectories of dummies so that the adversaries cannot distinguish the user from dummies. We conduct extensive experiments using real map information, and the results show the efficiency and effectiveness of Edge. |
| first_indexed | 2024-12-13T13:23:09Z |
| format | Article |
| id | doaj.art-b02fb4a8be604080a1a4abfdc3a60d16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:23:09Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b02fb4a8be604080a1a4abfdc3a60d162022-12-21T23:44:21ZengIEEEIEEE Access2169-35362018-01-016229582296910.1109/ACCESS.2018.28298988347080Dummy Generation Based on User-Movement Estimation for Location Privacy ProtectionShuhei Hayashida0https://orcid.org/0000-0002-2371-778XDaichi Amagata1https://orcid.org/0000-0001-8571-4931Takahiro Hara2Xing Xie3Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, JapanDepartment of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, JapanDepartment of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, JapanMicrosoft Research Asia, Beijing, ChinaLocation-based services (LBSs) have been becoming more common due to the prevalence of GPS-enabled devices. While LBSs bring many benefits for our daily lives, location information may reveal private information, rendering an important problem of protecting location privacy of users. To anonymize the locations of users, we focus on dummy-based approaches that generate dummies and their locations are sent along with the actual location of a user to an LBS provider. Although several existing studies developed dummy-based techniques, they assume unrealistic user mobility, e.g., users keep moving and do not stop or follow a pre-defined movement plan precisely. In this paper, we remove the unrealistic assumptions and require much easier input with respect to user movement, i.e., only a set of visiting points. Under the assumption, we propose a dummy generation method, estimation-based dummy trajectory generation (Edge). Based on the given visiting points, Edge estimates a user-movement plan and designs trajectories of dummies so that the adversaries cannot distinguish the user from dummies. We conduct extensive experiments using real map information, and the results show the efficiency and effectiveness of Edge.https://ieeexplore.ieee.org/document/8347080/Location-based serviceslocation privacy |
| spellingShingle | Shuhei Hayashida Daichi Amagata Takahiro Hara Xing Xie Dummy Generation Based on User-Movement Estimation for Location Privacy Protection IEEE Access Location-based services location privacy |
| title | Dummy Generation Based on User-Movement Estimation for Location Privacy Protection |
| title_full | Dummy Generation Based on User-Movement Estimation for Location Privacy Protection |
| title_fullStr | Dummy Generation Based on User-Movement Estimation for Location Privacy Protection |
| title_full_unstemmed | Dummy Generation Based on User-Movement Estimation for Location Privacy Protection |
| title_short | Dummy Generation Based on User-Movement Estimation for Location Privacy Protection |
| title_sort | dummy generation based on user movement estimation for location privacy protection |
| topic | Location-based services location privacy |
| url | https://ieeexplore.ieee.org/document/8347080/ |
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