Abstract
At present, location based service (LBS) has developed rapidly, it is extensive use in the applications of various intelligent mobile terminals. The trajectory privacy of users is protected by virtual trajectory generation algorithm constructed by statistical method. Since the user’s motion model is a complex equation, it is difficult to model it mathematically, because the user’s trajectory model does not consider its motion model, limiting the formation of the trajectory. As a result, previous virtual trajectory generation algorithms were not resistant to deep learning-based data mining attacks. In this paper, real and virtual trajectory discriminators are designed using LSTM (Long Short-Term Memory) technology, and a deep learning-based virtual trajectory generation scheme is proposed. Experiments show that the false trajectory can be identified with a success rate of at least 96%, while for the real trajectory, the false positive rate is only 6.5%. The virtual trajectory generated by the proposed scheme has human motion patterns similar to the real trajectory, and protects against colluding, inference, and channel attacks. The generated virtual trajectory points will not be distributed in the map inaccessible areas. On the premise that the users obtain the service quality, the user trajectory privacy is effectively protected to reduce the loss as much as possible.
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Pan, J., Yang, J., Fan, H., Liu, Y. (2022). Deep Learning-Based Virtual Trajectory Generation Scheme. In: Yang, S., Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2022. Communications in Computer and Information Science, vol 1700. Springer, Singapore. https://doi.org/10.1007/978-981-19-7946-0_15
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DOI: https://doi.org/10.1007/978-981-19-7946-0_15
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