  As privacy gains traction in the NLP community, researchers have started
adopting various approaches to privacy-preserving methods. One of the favorite
privacy frameworks, differential privacy (DP), is perhaps the most compelling
thanks to its fundamental theoretical guarantees. Despite the apparent
simplicity of the general concept of differential privacy, it seems non-trivial
to get it right when applying it to NLP. In this short paper, we formally
analyze several recent NLP papers proposing text representation learning using
DPText (Beigi et al., 2019a,b; Alnasser et al., 2021; Beigi et al., 2021) and
reveal their false claims of being differentially private. Furthermore, we also
show a simple yet general empirical sanity check to determine whether a given
implementation of a DP mechanism almost certainly violates the privacy loss
guarantees. Our main goal is to raise awareness and help the community
understand potential pitfalls of applying differential privacy to text
representation learning.
