Abstract
Location traces are useful for various types of geo-data analysis tasks, and synthesizing location traces is a promising approach to geo-data analysis while protecting user privacy. However, existing location synthesizers do not consider friendship information of users. In particular, a co-location between friends is an important factor for synthesizing more realistic location traces.
In this paper, we propose a novel location synthesizer that generates synthetic traces including co-locations between friends. Our synthesizer models the information about the co-locations by two parameters: friendship probability and co-location count matrix. The friendship probability represents a probability that two users will be a friend, whereas the co-location count matrix comprises a co-location count for each time instant and each location. Our synthesizer also provides DP (Differential Privacy) for training data. We evaluate our synthesizer using the Foursquare dataset. Our experimental results show that our synthesizer preserves the information about co-locations and other statistical information (e.g., population distribution, transition matrix) while providing DP with a reasonable privacy budget (e.g., smaller than 1).
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Narita, J., Suganuma, Y., Nishigaki, M., Murakami, T., Ohki, T. (2022). Synthesizing Privacy-Preserving Location Traces Including Co-locations. In: Garcia-Alfaro, J., Muñoz-Tapia, J.L., Navarro-Arribas, G., Soriano, M. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2021 2021. Lecture Notes in Computer Science(), vol 13140. Springer, Cham. https://doi.org/10.1007/978-3-030-93944-1_2
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DOI: https://doi.org/10.1007/978-3-030-93944-1_2
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