Understanding the Tradeoffs in Client-Side Privacy for Speech Recognition

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.