  With the use of personal devices connected to the Internet for tasks such as
searches and shopping becoming ubiquitous, ensuring the privacy of the users of
such services has become a requirement in order to build and maintain customer
trust. While text privatization methods exist, they require the existence of a
trusted party that collects user data before applying a privatization method to
preserve users' privacy. In this work we propose an efficient mechanism to
provide metric differential privacy for text data on-device. With our solution,
sensitive data never leaves the device and service providers only have access
to privatized data to train models on and analyze. We compare our algorithm to
the state-of-the-art for text privatization, showing similar or better utility
for the same privacy guarantees, while reducing the storage costs by orders of
magnitude, enabling on-device text privatization.
