  Accurately learning from user data while providing quantifiable privacy
guarantees provides an opportunity to build better ML models while maintaining
user trust. This paper presents a formal approach to carrying out privacy
preserving text perturbation using the notion of dx-privacy designed to achieve
geo-indistinguishability in location data. Our approach applies carefully
calibrated noise to vector representation of words in a high dimension space as
defined by word embedding models. We present a privacy proof that satisfies
dx-privacy where the privacy parameter epsilon provides guarantees with respect
to a distance metric defined by the word embedding space. We demonstrate how
epsilon can be selected by analyzing plausible deniability statistics backed up
by large scale analysis on GloVe and fastText embeddings. We conduct privacy
audit experiments against 2 baseline models and utility experiments on 3
datasets to demonstrate the tradeoff between privacy and utility for varying
values of epsilon on different task types. Our results demonstrate practical
utility (< 2% utility loss for training binary classifiers) while providing
better privacy guarantees than baseline models.
