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• W4318185106 abstract "In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs). To preserve UeDP, we developed a novel algorithm, called UeDP-Alg, optimizing the trade-off between privacy loss and model utility with a tight sensitivity bound derived from seamlessly combining user and sensitive entity sampling processes. An extensive theoretical analysis and evaluation show that our UeDP-Alg outperforms baseline approaches in model utility under the same privacy budget consumption on several NLM tasks, using benchmark datasets." @default.
• W4318185106 created "2023-01-27" @default.
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• W4318185106 date "2022-12-17" @default.
• W4318185106 modified "2023-09-27" @default.
• W4318185106 title "User-Entity Differential Privacy in Learning Natural Language Models" @default.
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• W4318185106 doi "https://doi.org/10.1109/bigdata55660.2022.10020247" @default.
• W4318185106 hasPublicationYear "2022" @default.
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