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Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels
In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term.
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