  This paper firstly proposes a simple yet efficient generalized approach to
apply differential privacy to text representation (i.e., word embedding). Based
on it, we propose a user-level approach to learn personalized differentially
private word embedding model on user generated contents (UGC). To our best
knowledge, this is the first work of learning user-level differentially private
word embedding model from text for sharing. The proposed approaches protect the
privacy of the individual from re-identification, especially provide better
trade-off of privacy and data utility on UGC data for sharing. The experimental
results show that the trained embedding models are applicable for the classic
text analysis tasks (e.g., regression). Moreover, the proposed approaches of
learning differentially private embedding models are both framework- and data-
independent, which facilitates the deployment and sharing. The source code is
available at https://github.com/sonvx/dpText.
