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• W2904416043 abstract "In recent years, data mining on spatial-temporal data has become a hot research issue due to the potential enormous value contained in the data. However, by combining the context information from sign-in services in social network, the usage of spatial-temporal data may lead to serious privacy disclosure even the data are anonymized. Neither k-anonymity locations nor k-anonymity path approaches can address the implicit privacy disclosure. In this paper, we propose to cluster users who have similar displacements in similar time periods by hierarchy clustering algorithm using Frechet distance first, and then improve k-anonymity method by minimizing the total information loss of location, time as well as clustering results. The experiment using public dataset GEOlife shows that the proposed approach can reduce the number of users with implicit privacy disclosure by considering the similarity of users' trajectories, in the cost of increasing total information loss of time while keeping that of location unchanged." @default.
• W2904416043 created "2018-12-22" @default.
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• W2904416043 date "2018-10-01" @default.
• W2904416043 modified "2023-09-26" @default.
• W2904416043 title "An Approach to Reducing Implicit Privacy Disclosure in Spatial-Temporal Big Data Publishing" @default.
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• W2904416043 doi "https://doi.org/10.1109/smartworld.2018.00193" @default.
• W2904416043 hasPublicationYear "2018" @default.
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