Privacy-preserving location-based traffic density monitoring
Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information o...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1993137 |
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| _version_ | 1850250180037705728 |
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| author | Lei Wu Xia Wei Lingzhen Meng Shengnan Zhao Hao Wang |
| author_facet | Lei Wu Xia Wei Lingzhen Meng Shengnan Zhao Hao Wang |
| author_sort | Lei Wu |
| collection | DOAJ |
| description | Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level. |
| format | Article |
| id | doaj-art-922d1eacc14a471ca739bcf883cb5f4a |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-922d1eacc14a471ca739bcf883cb5f4a2025-08-20T01:58:17ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134187489410.1080/09540091.2021.19931371993137Privacy-preserving location-based traffic density monitoringLei Wu0Xia Wei1Lingzhen Meng2Shengnan Zhao3Hao Wang4Shandong Normal UniversityShandong Normal UniversityShandong Normal UniversityShandong Normal UniversityShandong Normal UniversityTraffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.http://dx.doi.org/10.1080/09540091.2021.1993137location-based servicestraffic density monitoringk-anonymitydummy locationprivacy protection |
| spellingShingle | Lei Wu Xia Wei Lingzhen Meng Shengnan Zhao Hao Wang Privacy-preserving location-based traffic density monitoring Connection Science location-based services traffic density monitoring k-anonymity dummy location privacy protection |
| title | Privacy-preserving location-based traffic density monitoring |
| title_full | Privacy-preserving location-based traffic density monitoring |
| title_fullStr | Privacy-preserving location-based traffic density monitoring |
| title_full_unstemmed | Privacy-preserving location-based traffic density monitoring |
| title_short | Privacy-preserving location-based traffic density monitoring |
| title_sort | privacy preserving location based traffic density monitoring |
| topic | location-based services traffic density monitoring k-anonymity dummy location privacy protection |
| url | http://dx.doi.org/10.1080/09540091.2021.1993137 |
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