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Generating Labeled Multiple Attribute Trajectory Data With Selective Partial Anonymization Based on Exceptional Conditional Generative Adversarial Network
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DOI：10.1109/ACCESS.2023.3326246.png)
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Abstract
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Trajectory data generated in location-based service environments contain highly sensitive personal information, making them a prime target for privacy attacks. At the same time, however, valuable statistical information can be obtained from such private data. Optimizing this tradeoff between utility and privacy presents a challenge. This study introduces a novel method for partially anonymizing sensitive areas using a conditional generative adversarial network. The proposed method enables the learning of complex spatial, temporal, and categorical features of the selected sensitive area through the utilization of our condition label structure and loss function. In this study, we evaluate and analyze the contents by considering the spatial-temporal characteristics and dividing them into spatial usability and temporal usability. The experimental results demonstrate that the proposed method outperforms related models that employ generative adversarial networks. We achieved high scores in a majority of spatial evaluation items while also discussing the aspects that obtained relatively low scores.
Keywords:
Generative adversarial network
privacy protection
trajectory data
generating data
exceptional conditional
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3.6
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