Abstract
The high privacy and accuracy of trajectory data make data integrity and security critical. However, existing algorithms cannot ensure the integrity of temporal attributes and resist common operations in the normal use of data, which limits the use of data. For this reason, this paper proposes a semi-fragile water-marking algorithm for trajectory data that takes into account spatiotemporal features. The proposed algorithm utilizes the minimum area bounded rectangle (MABR) to group the trajectory data. Finally, the fragile watermarks generated from spatial and temporal attributes are embedded into an embedded domain with geometric invariance in stages using the multiple quantization index modulation (MQIM) technique. Experimental results show that the proposed algorithm is extremely robust to geometric attacks. Meanwhile, it can accurately identify whether the temporal and spatial attributes have been tampered with and the type of tampering. The proposed algorithm balances robustness and tampering detection capability, providing a feasible solution for the security protection of trajectory data.
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Disclosure of Interests
No conflict of interest exists relating to this article, which is approved by all authors for publication. The work described is original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part.
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Acknowledgments
This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFC3803600), the National Nature Science Foundation of China (Grant Nos. 41971338 and 42071362), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX22_1584).
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Hu, Y., Zhu, C., Ren, N., Gu, J. (2024). Trajectory Data Semi-fragile Watermarking Algorithm Considering Spatiotemporal Features. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_23
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