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- W4322096716 abstract "This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and data-independent, which facilitates the deployment and sharing. The source code will be available when the paper is published." @default.
- W4322096716 created "2023-02-26" @default.
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- W4322096716 date "2023-01-01" @default.
- W4322096716 modified "2023-09-27" @default.
- W4322096716 title "dpUGC: Learn Differentially Private Representation for User Generated Contents (Best Paper Award, Third Place, Shared)" @default.
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- W4322096716 doi "https://doi.org/10.1007/978-3-031-24337-0_23" @default.
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