Toward location privacy protection in Spatial crowdsourcing
Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, wh...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719830568 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559255313448960 |
|---|---|
| author | Hang Ye Kai Han Chaoting Xu Jingxin Xu Fei Gui |
| author_facet | Hang Ye Kai Han Chaoting Xu Jingxin Xu Fei Gui |
| author_sort | Hang Ye |
| collection | DOAJ |
| description | Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers’ locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework. |
| format | Article |
| id | doaj-art-01ee3d11412943019b0365e50c625290 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-01ee3d11412943019b0365e50c6252902025-02-03T01:30:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-03-011510.1177/1550147719830568Toward location privacy protection in Spatial crowdsourcingHang YeKai HanChaoting XuJingxin XuFei GuiSpatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers’ locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework.https://doi.org/10.1177/1550147719830568 |
| spellingShingle | Hang Ye Kai Han Chaoting Xu Jingxin Xu Fei Gui Toward location privacy protection in Spatial crowdsourcing International Journal of Distributed Sensor Networks |
| title | Toward location privacy protection in Spatial crowdsourcing |
| title_full | Toward location privacy protection in Spatial crowdsourcing |
| title_fullStr | Toward location privacy protection in Spatial crowdsourcing |
| title_full_unstemmed | Toward location privacy protection in Spatial crowdsourcing |
| title_short | Toward location privacy protection in Spatial crowdsourcing |
| title_sort | toward location privacy protection in spatial crowdsourcing |
| url | https://doi.org/10.1177/1550147719830568 |
| work_keys_str_mv | AT hangye towardlocationprivacyprotectioninspatialcrowdsourcing AT kaihan towardlocationprivacyprotectioninspatialcrowdsourcing AT chaotingxu towardlocationprivacyprotectioninspatialcrowdsourcing AT jingxinxu towardlocationprivacyprotectioninspatialcrowdsourcing AT feigui towardlocationprivacyprotectioninspatialcrowdsourcing |