  Existing approaches to ensuring privacy of user speech data primarily focus
on server-side approaches. While improving server-side privacy reduces certain
security concerns, users still do not retain control over whether privacy is
ensured on the client-side. In this paper, we define, evaluate, and explore
techniques for client-side privacy in speech recognition, where the goal is to
preserve privacy on raw speech data before leaving the client's device. We
first formalize several tradeoffs in ensuring client-side privacy between
performance, compute requirements, and privacy. Using our tradeoff analysis, we
perform a large-scale empirical study on existing approaches and find that they
fall short on at least one metric. Our results call for more research in this
crucial area as a step towards safer real-world deployment of speech
recognition systems at scale across mobile devices.
