STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion
There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy-preserving vehicle...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | IET Information Security |
| Online Access: | http://dx.doi.org/10.1049/ise2/1915019 |
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| _version_ | 1849228516999036928 |
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| author | Lei Tang Zhengxin Cao Xin Zhou Junzhe Zhang Junchi Ma |
| author_facet | Lei Tang Zhengxin Cao Xin Zhou Junzhe Zhang Junchi Ma |
| author_sort | Lei Tang |
| collection | DOAJ |
| description | There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy-preserving vehicle assignment strategy via spatial–temporal fusion (STF-LPPVA). Specifically, the strategy allows the cloud platform to train and distribute a spatial–temporal representation model to the user side. Encoded by this model, drivers and passengers can privately fuze the spatial–temporal information of their trips and then transmit these fuzed vectors to the cloud platform. Based on the similarity of the vectors, the cloud platform can allocate vehicles using the Kuhn–Monkreth (KM) algorithm. In addition, we analyze the theoretical feasibility of the STF-LPPVA strategy using entropy change and get good performance with a dataset from DiDi in Chengdu, China. The results show that the successful matching rate of the STF-LPPVA strategy is very close to the original data matching with lower time overhead. Our approach can reduce the traveling distance by 66.5% and improve the matching success rate by 36.2% on average. |
| format | Article |
| id | doaj-art-7cfefb06b9744b83b2c4abfb537f510f |
| institution | Kabale University |
| issn | 1751-8717 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj-art-7cfefb06b9744b83b2c4abfb537f510f2025-08-23T00:00:03ZengWileyIET Information Security1751-87172025-01-01202510.1049/ise2/1915019STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal FusionLei Tang0Zhengxin Cao1Xin Zhou2Junzhe Zhang3Junchi Ma4School of Information EngineeringSchool of Information EngineeringSchool of AutomationSchool of Information EngineeringSchool of Information EngineeringThere are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy-preserving vehicle assignment strategy via spatial–temporal fusion (STF-LPPVA). Specifically, the strategy allows the cloud platform to train and distribute a spatial–temporal representation model to the user side. Encoded by this model, drivers and passengers can privately fuze the spatial–temporal information of their trips and then transmit these fuzed vectors to the cloud platform. Based on the similarity of the vectors, the cloud platform can allocate vehicles using the Kuhn–Monkreth (KM) algorithm. In addition, we analyze the theoretical feasibility of the STF-LPPVA strategy using entropy change and get good performance with a dataset from DiDi in Chengdu, China. The results show that the successful matching rate of the STF-LPPVA strategy is very close to the original data matching with lower time overhead. Our approach can reduce the traveling distance by 66.5% and improve the matching success rate by 36.2% on average.http://dx.doi.org/10.1049/ise2/1915019 |
| spellingShingle | Lei Tang Zhengxin Cao Xin Zhou Junzhe Zhang Junchi Ma STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion IET Information Security |
| title | STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion |
| title_full | STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion |
| title_fullStr | STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion |
| title_full_unstemmed | STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion |
| title_short | STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion |
| title_sort | stf lppva local privacy preserving method for vehicle assignment based on spatial temporal fusion |
| url | http://dx.doi.org/10.1049/ise2/1915019 |
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