  Metric Differential Privacy is a generalization of differential privacy
tailored to address the unique challenges of text-to-text privatization. By
adding noise to the representation of words in the geometric space of
embeddings, words are replaced with words located in the proximity of the noisy
representation. Since embeddings are trained based on word co-occurrences, this
mechanism ensures that substitutions stem from a common semantic context.
Without considering the grammatical category of words, however, this mechanism
cannot guarantee that substitutions play similar syntactic roles. We analyze
the capability of text-to-text privatization to preserve the grammatical
category of words after substitution and find that surrogate texts consist
almost exclusively of nouns. Lacking the capability to produce surrogate texts
that correlate with the structure of the sensitive texts, we encompass our
analysis by transforming the privatization step into a candidate selection
problem in which substitutions are directed to words with matching grammatical
properties. We demonstrate a substantial improvement in the performance of
downstream tasks by up to $4.66\%$ while retaining comparative privacy
guarantees.
