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- W2080201182 abstract "The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (ε; δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (ε; δ)-differential privacy mechanism with the standard ε-differential privacy mechanism." @default.
- W2080201182 created "2016-06-24" @default.
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- W2080201182 date "2012-06-01" @default.
- W2080201182 modified "2023-09-27" @default.
- W2080201182 title "Preserving privacy for moving objects data mining" @default.
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- W2080201182 doi "https://doi.org/10.1109/isi.2012.6284198" @default.
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