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- Publisher Website: 10.1109/TVT.2016.2554608
- Scopus: eid_2-s2.0-85013083692
- WOS: WOS:000395740300058
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Article: Privacy-Preserving Social Tie Discovery Based on Cloaked Human Trajectories
Title | Privacy-Preserving Social Tie Discovery Based on Cloaked Human Trajectories |
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Authors | |
Keywords | Privacy preserving Social tie discovery Semantic similarity Cloaked trajectory |
Issue Date | 2017 |
Citation | IEEE Transactions on Vehicular Technology, 2017, v. 66, n. 2, p. 1619-1630 How to Cite? |
Abstract | © 2016 IEEE. The discovery of peoples' social connections is becoming a flourishing research topic, considering the rich social information inferable from human trajectories. Existing social tie detection methods often require mobile users to upload their accurate locations, causing serious privacy concerns. On the other hand, cloaking methods allow users to upload their obscured locations instead and can efficiently protect their location privacy. However, no existing social tie detection method can generate social relationships among users when only obscured trajectories are provided. To tackle the aforementioned problem, this paper proposes a novel semantic-tree-based algorithm. Specifically, we model the obscured regions from the cloaking algorithm as a semantic region tree and assign weight values for regions based on their popularity, further indicating the similarity between users based on their temporal and spatial relations. We evaluate our proposed approach using a real trajectory data set and show that our algorithm can identify social ties successfully with 20% higher accuracy than the existing approaches. |
Persistent Identifier | http://hdl.handle.net/10722/281460 |
ISSN | 2023 Impact Factor: 6.1 2023 SCImago Journal Rankings: 2.714 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Tian, Ye | - |
dc.contributor.author | Wang, Wendong | - |
dc.contributor.author | Wu, Jie | - |
dc.contributor.author | Kou, Qinli | - |
dc.contributor.author | Song, Zheng | - |
dc.contributor.author | Ngai, Edith C.H. | - |
dc.date.accessioned | 2020-03-13T10:37:55Z | - |
dc.date.available | 2020-03-13T10:37:55Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Vehicular Technology, 2017, v. 66, n. 2, p. 1619-1630 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.uri | http://hdl.handle.net/10722/281460 | - |
dc.description.abstract | © 2016 IEEE. The discovery of peoples' social connections is becoming a flourishing research topic, considering the rich social information inferable from human trajectories. Existing social tie detection methods often require mobile users to upload their accurate locations, causing serious privacy concerns. On the other hand, cloaking methods allow users to upload their obscured locations instead and can efficiently protect their location privacy. However, no existing social tie detection method can generate social relationships among users when only obscured trajectories are provided. To tackle the aforementioned problem, this paper proposes a novel semantic-tree-based algorithm. Specifically, we model the obscured regions from the cloaking algorithm as a semantic region tree and assign weight values for regions based on their popularity, further indicating the similarity between users based on their temporal and spatial relations. We evaluate our proposed approach using a real trajectory data set and show that our algorithm can identify social ties successfully with 20% higher accuracy than the existing approaches. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Vehicular Technology | - |
dc.subject | Privacy preserving | - |
dc.subject | Social tie discovery | - |
dc.subject | Semantic similarity | - |
dc.subject | Cloaked trajectory | - |
dc.title | Privacy-Preserving Social Tie Discovery Based on Cloaked Human Trajectories | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TVT.2016.2554608 | - |
dc.identifier.scopus | eid_2-s2.0-85013083692 | - |
dc.identifier.volume | 66 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1619 | - |
dc.identifier.epage | 1630 | - |
dc.identifier.isi | WOS:000395740300058 | - |
dc.identifier.issnl | 0018-9545 | - |