CCS2020

Voice-Indistinguishability - Protecting Voiceprint with Differential Privacy under an Untrusted Server

Yaowei Han, Yang Cao, Sheng Li, Qiang Ma, Masatoshi Yoshikawa

9 citations

Abstract

With the rising adoption of advanced voice-based technology together with increasing consumer demand for smart devices, voice-controlled "virtual assistants" such as Apple's Siri and Google Assistant have been integrated into people's daily lives. However, privacy and security concerns may hinder the development of such voice-based applications since speech data contain the speaker's biometric identifier, i.e., voiceprint (as analogous to fingerprint). To alleviate privacy concerns in speech data collection, we propose a fast speech data de-identification system that allows a user to share her speech data with formal privacy guarantee to an untrusted server. Our open-sourced system can be easily integrated into other speech processing systems for collecting users' voice data in a privacy-preserving way. Experiments on public datasets verify the effectiveness and efficiency of the proposed system.