Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors
Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our prop...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1314 |
| _version_ | 1797297005617217536 |
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| author | Yongjun Li Yuefei Zhu Jinlong Fei Wei Wu |
| author_facet | Yongjun Li Yuefei Zhu Jinlong Fei Wei Wu |
| author_sort | Yongjun Li |
| collection | DOAJ |
| description | Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors’ preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection. |
| first_indexed | 2024-03-07T22:13:58Z |
| format | Article |
| id | doaj.art-7174601f2d0a410f9ed0d8d13326d7f2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:13:58Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7174601f2d0a410f9ed0d8d13326d7f22024-02-23T15:34:08ZengMDPI AGSensors1424-82202024-02-01244131410.3390/s24041314Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal FactorsYongjun Li0Yuefei Zhu1Jinlong Fei2Wei Wu3School of Cyberspace Security, Information Engineering University, Zhengzhou 450002, ChinaSchool of Cyberspace Security, Information Engineering University, Zhengzhou 450002, ChinaSchool of Cyberspace Security, Information Engineering University, Zhengzhou 450002, ChinaSchool of Cyberspace Security, Information Engineering University, Zhengzhou 450002, ChinaAddressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors’ preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.https://www.mdpi.com/1424-8220/24/4/1314location-based serviceslocation privacygeographical informationsemantic informationtemporal informationenhanced distinguishability metrics |
| spellingShingle | Yongjun Li Yuefei Zhu Jinlong Fei Wei Wu Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors Sensors location-based services location privacy geographical information semantic information temporal information enhanced distinguishability metrics |
| title | Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors |
| title_full | Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors |
| title_fullStr | Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors |
| title_full_unstemmed | Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors |
| title_short | Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors |
| title_sort | diverse metrics for robust lbs privacy distance semantics and temporal factors |
| topic | location-based services location privacy geographical information semantic information temporal information enhanced distinguishability metrics |
| url | https://www.mdpi.com/1424-8220/24/4/1314 |
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