  Privacy is an important concern when building statistical models on data
containing personal information. Differential privacy offers a strong
definition of privacy and can be used to solve several privacy concerns (Dwork
et al., 2014). Multiple solutions have been proposed for the
differentially-private transformation of datasets containing sensitive
information. However, such transformation algorithms offer poor utility in
Natural Language Processing (NLP) tasks due to noise added in the process. In
this paper, we address this issue by providing a utility-preserving
differentially private text transformation algorithm using auto-encoders. Our
algorithm transforms text to offer robustness against attacks and produces
transformations with high semantic quality that perform well on downstream NLP
tasks. We prove the theoretical privacy guarantee of our algorithm and assess
its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al.,
2017) on models trained with transformed data. Our results show that the
proposed model performs better against MIA attacks while offering lower to no
degradation in the utility of the underlying transformation process compared to
existing baselines.
