2022
Differentially Private Language Models for Secure Data Sharing
EMNLP 2022main
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 buildi…