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Semantics-Preserved Distortion for Personal Privacy Protection. (arXiv:2201.00965v1 [cs.CL])
Jan. 5, 2022, 2:10 a.m. | Letian Peng, Zuchao Li, Hai Zhao
cs.CL updates on arXiv.org arxiv.org
Privacy protection is an important and concerning topic in Federated
Learning, especially for Natural Language Processing. In client devices, a
large number of texts containing personal information are produced by users
every day. As the direct application of information from users is likely to
invade personal privacy, many methods have been proposed in Federated Learning
to block the center model from the raw information in client devices. In this
paper, we try to do this more linguistically via distorting the …
More from arxiv.org / cs.CL updates on arXiv.org
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