Survey of differentially private methods for trajectory data
With the rapid development of sensor and positioning technologies, vast amounts of trajectory data were generated, stored, and shared by users’ smart mobile devices. These data contained valuable personal spatiotemporal mobility features, which could be leveraged by businesses and government agencie...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2025-06-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025027 |
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| Summary: | With the rapid development of sensor and positioning technologies, vast amounts of trajectory data were generated, stored, and shared by users’ smart mobile devices. These data contained valuable personal spatiotemporal mobility features, which could be leveraged by businesses and government agencies to provide efficient and convenient services to users and society. However, individual trajectory data were private and sensitive, and improper data usage could not only expose users’ home and work addresses but also reveal their health status and economic conditions. These privacy concerns led to reluctance in sharing trajectory data, hindering the development of location-based services and applications. To address this issue, a promising solution—differential privacy (DP) technology—was proposed. DP could offer rigorous, provable privacy guarantees for users’ sensitive data while preserving valuable information. The research progress of DP in protecting trajectory data privacy was reviewed. The DP methods for trajectory data were analyzed from perspectives such as privacy models, application scenarios, and perturbation mechanisms. Finally, an outlook on the future development of DP for trajectory data privacy protection was provided. |
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| ISSN: | 2096-109X |