A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service
Location-based services (LBS), as one of the most popular location-awareness applications, has been further developed to achieve low-latency with the assistance of fog computing. However, privacy issues remain a research challenge in the context of fog computing. Therefore, in this paper, we present...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/7/1611 |
| _version_ | 1798024686345388032 |
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| author | Xue Yang Fan Yin Xiaohu Tang |
| author_facet | Xue Yang Fan Yin Xiaohu Tang |
| author_sort | Xue Yang |
| collection | DOAJ |
| description | Location-based services (LBS), as one of the most popular location-awareness applications, has been further developed to achieve low-latency with the assistance of fog computing. However, privacy issues remain a research challenge in the context of fog computing. Therefore, in this paper, we present a fine-grained and privacy-preserving query scheme for fog computing-enhanced location-based services, hereafter referred to as FGPQ. In particular, mobile users can obtain the fine-grained searching result satisfying not only the given spatial range but also the searching content. Detailed privacy analysis shows that our proposed scheme indeed achieves the privacy preservation for the LBS provider and mobile users. In addition, extensive performance analyses and experiments demonstrate that the FGPQ scheme can significantly reduce computational and communication overheads and ensure the low-latency, which outperforms existing state-of-the art schemes. Hence, our proposed scheme is more suitable for real-time LBS searching. |
| first_indexed | 2024-04-11T18:06:40Z |
| format | Article |
| id | doaj.art-aa59d1b2ce434ffda5f66b0d2eabbc36 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:06:40Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa59d1b2ce434ffda5f66b0d2eabbc362022-12-22T04:10:19ZengMDPI AGSensors1424-82202017-07-01177161110.3390/s17071611s17071611A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based ServiceXue Yang0Fan Yin1Xiaohu Tang2The Information Security and National Computing Grid Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaThe Information Security and National Computing Grid Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaThe Information Security and National Computing Grid Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaLocation-based services (LBS), as one of the most popular location-awareness applications, has been further developed to achieve low-latency with the assistance of fog computing. However, privacy issues remain a research challenge in the context of fog computing. Therefore, in this paper, we present a fine-grained and privacy-preserving query scheme for fog computing-enhanced location-based services, hereafter referred to as FGPQ. In particular, mobile users can obtain the fine-grained searching result satisfying not only the given spatial range but also the searching content. Detailed privacy analysis shows that our proposed scheme indeed achieves the privacy preservation for the LBS provider and mobile users. In addition, extensive performance analyses and experiments demonstrate that the FGPQ scheme can significantly reduce computational and communication overheads and ensure the low-latency, which outperforms existing state-of-the art schemes. Hence, our proposed scheme is more suitable for real-time LBS searching.https://www.mdpi.com/1424-8220/17/7/1611location-based services (LBS)fog computinglow-latencyfine-grainedprivacy-preserving |
| spellingShingle | Xue Yang Fan Yin Xiaohu Tang A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service Sensors location-based services (LBS) fog computing low-latency fine-grained privacy-preserving |
| title | A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service |
| title_full | A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service |
| title_fullStr | A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service |
| title_full_unstemmed | A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service |
| title_short | A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service |
| title_sort | fine grained and privacy preserving query scheme for fog computing enhanced location based service |
| topic | location-based services (LBS) fog computing low-latency fine-grained privacy-preserving |
| url | https://www.mdpi.com/1424-8220/17/7/1611 |
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