TEM: High Utility Metric Differential Privacy on Text

Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan, Ke Wang

Abstract:

Ensuring the privacy of users whose data are used
to train Natural Language Processing (NLP) models
is necessary to build and maintain customer trust.
Differential Privacy (DP) has emerged as the most
successful method to protect the privacy of individuals.
However, applying DP to the NLP domain comes with
unique challenges. The most successful previous methods
use a generalization of DP for metric spaces, and apply
the privatization by adding noise to inputs in the metric
space of word embeddings. However, these methods
assume that one specific distance measure is being used,
ignore the density of the space around the input, and
assume the embeddings used have been trained on public
data. In this work we propose Truncated Exponential
Mechanism (TEM), a general method that allows the
privatization of words using any distance metric, on
embeddings that can be trained on sensitive data. Our
method makes use of the exponential mechanism to
turn the privatization step into a selection problem.
This allows the noise applied to be calibrated to the
density of the embedding space around the input, and
makes domain adaptation possible for the embeddings.
In our experiments, we demonstrate that our method
outperforms the state-of-the-art in terms of utility for
the same level of privacy, while providing more flexibility
in the metric selection.