  With the increasing applications of language models, it has become crucial to
protect these models from leaking private information. Previous work has
attempted to tackle this challenge by training RNN-based language models with
differential privacy guarantees. However, applying classical differential
privacy to language models leads to poor model performance as the underlying
privacy notion is over-pessimistic and provides undifferentiated protection for
all tokens in the data. Given that the private information in natural language
is sparse (for example, the bulk of an email might not carry personally
identifiable information), we propose a new privacy notion, selective
differential privacy, to provide rigorous privacy guarantees on the sensitive
portion of the data to improve model utility. To realize such a new notion, we
develop a corresponding privacy mechanism, Selective-DPSGD, for RNN-based
language models. Besides language modeling, we also apply the method to a more
concrete application--dialog systems. Experiments on both language modeling and
dialog system building show that the proposed privacy-preserving mechanism
achieves better utilities while remaining safe under various privacy attacks
compared to the baselines. The data and code are released at
https://github.com/wyshi/lm_privacy to facilitate future research .
