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BRR: Preserving Privacy of Text Data Efficiently on Device
Ricardo Silva Carvalho · Theodore Vasiloudis · Oluwaseyi Feyisetan

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.

Author Information

Ricardo Silva Carvalho (Simon Fraser University)
Theodore Vasiloudis (Amazon.com)

I completed my PhD on the topic of "Large-scale Machine Learning through Approximation and Distributed Computing" at the Royal Institute of Technology, KTH in Stockholm. Before that I did my MSc in Machine Learning at KTH as well, completing my thesis at Spotify with the topic "Extending recommendation algorithms by modeling user context". During my PhD I completed internships at Data Artisans, Pandora Media, and Amazon. I am currently an Applied Scientist for Amazon, working in the Search Science and AI group

Oluwaseyi Feyisetan (Amazon)

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