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• W3093155323 abstract "A huge amount of sensitive personal data is being collected by various online health monitoring applications. Although the data is anonymous, the personal trajectories (e.g., the chronological access records of small cells) could become the anchor of linkage attacks to re-identify the users. Focusing on trajectory privacy in online health monitoring, we propose the User Trajectory Model (UTM), a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data (e.g., number of individuals covered by each small cell), and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness (i.e., the expectation and the variance). Eventually, exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation, confirm our theoretical deductions and present counter-intuitive insights." @default.
• W3093155323 created "2020-10-22" @default.
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• W3093155323 date "2021-08-01" @default.
• W3093155323 modified "2023-10-16" @default.
• W3093155323 title "UTM: A trajectory privacy evaluating model for online health monitoring" @default.
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• W3093155323 doi "https://doi.org/10.1016/j.dcan.2020.10.001" @default.
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