Differentially Private Language Models for Secure Data Sharing. (arXiv:2210.13918v1 [cs.LG])

To protect the privacy of individuals whose data is being shared, it is of
high importance to develop methods allowing researchers and companies to
release textual data while providing formal privacy guarantees to its
originators. In the field of NLP, substantial efforts have been directed at
building mechanisms following the framework of local differential privacy,
thereby anonymizing individual text samples before releasing them. In practice,
these approaches are often dissatisfying in terms of the quality of their
output language due …

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