  Accurately learning from user data while ensuring quantifiable privacy
guarantees provides an opportunity to build better Machine Learning (ML) models
while maintaining user trust. Recent literature has demonstrated the
applicability of a generalized form of Differential Privacy to provide
guarantees over text queries. Such mechanisms add privacy preserving noise to
vectorial representations of text in high dimension and return a text based
projection of the noisy vectors. However, these mechanisms are sub-optimal in
their trade-off between privacy and utility. This is due to factors such as a
fixed global sensitivity which leads to too much noise added in dense spaces
while simultaneously guaranteeing protection for sensitive outliers. In this
proposal paper, we describe some challenges in balancing the tradeoff between
privacy and utility for these differentially private text mechanisms. At a high
level, we provide two proposals: (1) a framework called LAC which defers some
of the noise to a privacy amplification step and (2), an additional suite of
three different techniques for calibrating the noise based on the local region
around a word. Our objective in this paper is not to evaluate a single solution
but to further the conversation on these challenges and chart pathways for
building better mechanisms.
