Accurately learning from user data while ensuring quantifiable privacy guarantees provides an opportunity to build better 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.
In this Graveur-Decoupeur Laser proposal paper, we describe some challenges in balancing this trade-off.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 water coloring 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.