SUDM-SP: A method for discovering trajectory similar users based on semantic privacy
With intelligent terminal devices’ widespread adoption and global positioning systems’ advancement, Location-based Social Networking Services (LbSNs) have gained considerable attention. The recommendation mechanism, which revolves around identifying similar users, holds significant importance in LbS...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | High-Confidence Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295223000442 |
| _version_ | 1827802951247200256 |
|---|---|
| author | Weiqi Zhang Guisheng Yin Bingyi Xie |
| author_facet | Weiqi Zhang Guisheng Yin Bingyi Xie |
| author_sort | Weiqi Zhang |
| collection | DOAJ |
| description | With intelligent terminal devices’ widespread adoption and global positioning systems’ advancement, Location-based Social Networking Services (LbSNs) have gained considerable attention. The recommendation mechanism, which revolves around identifying similar users, holds significant importance in LbSNs. In order to enhance user experience, LbSNs heavily rely on accurate data. By mining and analyzing users who exhibit similar behavioral patterns to the target user, LbSNs can offer personalized services that cater to individual preferences. However, trajectory data, a form encompassing various sensitive attributes, pose privacy concerns. Unauthorized disclosure of users’ precise trajectory information can have severe consequences, potentially impacting their daily lives. Thus, this paper proposes the Similar User Discovery Method based on Semantic Privacy (SUDM-SP) for trajectory analysis. The approach involves employing a model that generates noise trajectories, maximizing expected noise to preserve the privacy of the original trajectories. Similar users are then identified based on the published noise trajectory data. SUDM-SP consists of two key components. Firstly, a puppet noise location, exhibiting the highest semantic expectation with the original location, is generated to derive noise-suppressed trajectory data. Secondly, a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities, facilitating the discovery of noise trajectory similarity among users. Through trials conducted using real datasets, the effectiveness of SUDM-SP, as a recommendation service ensuring user privacy protection is substantiated. |
| first_indexed | 2024-03-11T20:48:22Z |
| format | Article |
| id | doaj.art-62ae6d086b0a4c78b48e294ad2366b1b |
| institution | Directory Open Access Journal |
| issn | 2667-2952 |
| language | English |
| last_indexed | 2024-03-11T20:48:22Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | High-Confidence Computing |
| spelling | doaj.art-62ae6d086b0a4c78b48e294ad2366b1b2023-10-01T06:03:41ZengElsevierHigh-Confidence Computing2667-29522023-09-0133100146SUDM-SP: A method for discovering trajectory similar users based on semantic privacyWeiqi Zhang0Guisheng Yin1Bingyi Xie2College of Computer Science and Technology, Harbin Engineering University, Harbin 150009, China; Corresponding author.College of Computer Science and Technology, Harbin Engineering University, Harbin 150009, ChinaDepartment of Computer Science, Georgia State University, GA 30303, USAWith intelligent terminal devices’ widespread adoption and global positioning systems’ advancement, Location-based Social Networking Services (LbSNs) have gained considerable attention. The recommendation mechanism, which revolves around identifying similar users, holds significant importance in LbSNs. In order to enhance user experience, LbSNs heavily rely on accurate data. By mining and analyzing users who exhibit similar behavioral patterns to the target user, LbSNs can offer personalized services that cater to individual preferences. However, trajectory data, a form encompassing various sensitive attributes, pose privacy concerns. Unauthorized disclosure of users’ precise trajectory information can have severe consequences, potentially impacting their daily lives. Thus, this paper proposes the Similar User Discovery Method based on Semantic Privacy (SUDM-SP) for trajectory analysis. The approach involves employing a model that generates noise trajectories, maximizing expected noise to preserve the privacy of the original trajectories. Similar users are then identified based on the published noise trajectory data. SUDM-SP consists of two key components. Firstly, a puppet noise location, exhibiting the highest semantic expectation with the original location, is generated to derive noise-suppressed trajectory data. Secondly, a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities, facilitating the discovery of noise trajectory similarity among users. Through trials conducted using real datasets, the effectiveness of SUDM-SP, as a recommendation service ensuring user privacy protection is substantiated.http://www.sciencedirect.com/science/article/pii/S2667295223000442Differential privacyLbSNsSemantic trajectoryRecommendation |
| spellingShingle | Weiqi Zhang Guisheng Yin Bingyi Xie SUDM-SP: A method for discovering trajectory similar users based on semantic privacy High-Confidence Computing Differential privacy LbSNs Semantic trajectory Recommendation |
| title | SUDM-SP: A method for discovering trajectory similar users based on semantic privacy |
| title_full | SUDM-SP: A method for discovering trajectory similar users based on semantic privacy |
| title_fullStr | SUDM-SP: A method for discovering trajectory similar users based on semantic privacy |
| title_full_unstemmed | SUDM-SP: A method for discovering trajectory similar users based on semantic privacy |
| title_short | SUDM-SP: A method for discovering trajectory similar users based on semantic privacy |
| title_sort | sudm sp a method for discovering trajectory similar users based on semantic privacy |
| topic | Differential privacy LbSNs Semantic trajectory Recommendation |
| url | http://www.sciencedirect.com/science/article/pii/S2667295223000442 |
| work_keys_str_mv | AT weiqizhang sudmspamethodfordiscoveringtrajectorysimilarusersbasedonsemanticprivacy AT guishengyin sudmspamethodfordiscoveringtrajectorysimilarusersbasedonsemanticprivacy AT bingyixie sudmspamethodfordiscoveringtrajectorysimilarusersbasedonsemanticprivacy |