A novel destination prediction attack and corresponding location privacy protection method in geo-social networks
Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not on...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716685421 |
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| _version_ | 1849403090776621056 |
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| author | Di Xue Li-Fa Wu Hua-Bo Li Zheng Hong Zhen-Ji Zhou |
| author_facet | Di Xue Li-Fa Wu Hua-Bo Li Zheng Hong Zhen-Ji Zhou |
| author_sort | Di Xue |
| collection | DOAJ |
| description | Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework. |
| format | Article |
| id | doaj-art-9894d46bb26349fd904a214c8d12a5d4 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-9894d46bb26349fd904a214c8d12a5d42025-08-20T03:37:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-01-011310.1177/1550147716685421A novel destination prediction attack and corresponding location privacy protection method in geo-social networksDi XueLi-Fa WuHua-Bo LiZheng HongZhen-Ji ZhouLocation publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework.https://doi.org/10.1177/1550147716685421 |
| spellingShingle | Di Xue Li-Fa Wu Hua-Bo Li Zheng Hong Zhen-Ji Zhou A novel destination prediction attack and corresponding location privacy protection method in geo-social networks International Journal of Distributed Sensor Networks |
| title | A novel destination prediction attack and corresponding location privacy protection method in geo-social networks |
| title_full | A novel destination prediction attack and corresponding location privacy protection method in geo-social networks |
| title_fullStr | A novel destination prediction attack and corresponding location privacy protection method in geo-social networks |
| title_full_unstemmed | A novel destination prediction attack and corresponding location privacy protection method in geo-social networks |
| title_short | A novel destination prediction attack and corresponding location privacy protection method in geo-social networks |
| title_sort | novel destination prediction attack and corresponding location privacy protection method in geo social networks |
| url | https://doi.org/10.1177/1550147716685421 |
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