Abstract
Location data shared on social media is collected and processed as trajectory data, which exposes individuals to leakage of sensitive information, such as sensitive geographic areas. A typical countermeasure is a generative adversarial network (GAN) model that ensures data anonymity. However, generating data selectively by identifying only sensitive areas is difficult. In this study, we propose an except-condition GAN (exGAN) model that generates synthetic data while maintaining the original’s utility. This model ensures the anonymity of sensitive areas and maintains the distribution of data in relatively less sensitive areas. It uses a method that assigns the remaining labels except for specific selected labels as a condition. The selected labels represent points of high sensitivity, and the trajectory data generated by the model contains only points corresponding to the labels, except for the selected labels. Furthermore, in our comparative evaluation of the exGAN model, it outperformed the original GAN model.
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Acknowledgments
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2021–0-00231, Development of Approximate DBMS Query Technology to Facilitate Fast Query Processing for Exploratory Data Analysis, 50%). This work was also supported by the National Research Foundation of Korea (NRF) granted by the Korean government (MSIT) (No. NRF-2021R1F1A1054739, 30%) and the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2023–2018-08–01417, 20%) supervised by the Institute for Information & Communications Technology Promotion (IITP).
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Song, Y., Shin, J., Ahn, J., Lee, T., Im, DH. (2023). Except-Condition Generative Adversarial Network for Generating Trajectory Data. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_23
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DOI: https://doi.org/10.1007/978-3-031-39821-6_23
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