Driving Context into Text-to-Text Privatization
Stefan Arnold Dilara Yesilbas Sven Weinzierl
\textit{Metric Differential Privacy} enables text-to-text privatization by
adding calibrated noise to the vector of a word derived from an embedding space
and projecting this noisy vector back to a discrete vocabulary using a nearest
neighbor search. Since words are substituted without context, this mechanism is
expected to fall short at finding substitutes for words with ambiguous
meanings, such as \textit{'bank'}. To account for these ambiguous words, we
leverage a sense embedding and incorporate a sense disambiguation step prior to
noise injection. We encompass our modification to the privatization mechanism
with an estimation of privacy and utility. For word sense disambiguation on the
\textit{Words in Context} dataset, we demonstrate a substantial increase in
classification accuracy by $6.05\%$.