Privacy Protection Method for Vehicle Trajectory Based on VLPR Data
With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becomi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6026140 |
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| _version_ | 1832547085971357696 |
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| author | Hua Chen Chen Xiong Jia-meng Xie Ming Cai |
| author_facet | Hua Chen Chen Xiong Jia-meng Xie Ming Cai |
| author_sort | Hua Chen |
| collection | DOAJ |
| description | With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures. |
| format | Article |
| id | doaj-art-748cd3fc06554aa8ba9f8bc83ed1852d |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-748cd3fc06554aa8ba9f8bc83ed1852d2025-02-03T06:46:09ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/60261406026140Privacy Protection Method for Vehicle Trajectory Based on VLPR DataHua Chen0Chen Xiong1Jia-meng Xie2Ming Cai3School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaTraffic Administration Bureau of Guangdong Province, Guangzhou 510440, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaWith the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures.http://dx.doi.org/10.1155/2020/6026140 |
| spellingShingle | Hua Chen Chen Xiong Jia-meng Xie Ming Cai Privacy Protection Method for Vehicle Trajectory Based on VLPR Data Journal of Advanced Transportation |
| title | Privacy Protection Method for Vehicle Trajectory Based on VLPR Data |
| title_full | Privacy Protection Method for Vehicle Trajectory Based on VLPR Data |
| title_fullStr | Privacy Protection Method for Vehicle Trajectory Based on VLPR Data |
| title_full_unstemmed | Privacy Protection Method for Vehicle Trajectory Based on VLPR Data |
| title_short | Privacy Protection Method for Vehicle Trajectory Based on VLPR Data |
| title_sort | privacy protection method for vehicle trajectory based on vlpr data |
| url | http://dx.doi.org/10.1155/2020/6026140 |
| work_keys_str_mv | AT huachen privacyprotectionmethodforvehicletrajectorybasedonvlprdata AT chenxiong privacyprotectionmethodforvehicletrajectorybasedonvlprdata AT jiamengxie privacyprotectionmethodforvehicletrajectorybasedonvlprdata AT mingcai privacyprotectionmethodforvehicletrajectorybasedonvlprdata |