  Using our voices to access, and interact with, online services raises
concerns about the trade-offs between convenience, privacy, and security. The
conflict between maintaining privacy and ensuring input authenticity has often
been hindered by the need to share raw data, which contains all the
paralinguistic information required to infer a variety of sensitive
characteristics. Users of voice assistants put their trust in service
providers; however, this trust is potentially misplaced considering the
emergence of first-party 'honest-but-curious' or 'semi-honest' threats. A
further security risk is presented by imposters gaining access to systems by
pretending to be the user leveraging replay or 'deepfake' attacks. Our
objective is to design and develop a new voice input-based system that offers
the following specifications: local authentication to reduce the need for
sharing raw voice data, local privacy preservation based on user preferences,
allowing more flexibility in integrating such a system given target
applications privacy constraints, and achieving good performance in these
targeted applications. The key idea is to locally derive token-based
credentials based on unique-identifying attributes obtained from the user's
voice and offer selective sensitive information filtering before transmitting
raw data. Our system consists of (i) 'VoiceID', boosted with a liveness
detection technology to thwart replay attacks; (ii) a flexible privacy filter
that allows users to select the level of privacy protection they prefer for
their data. The system yields 98.68% accuracy in verifying legitimate users
with cross-validation and runs in tens of milliseconds on a CPU and single-core
ARM processor without specialized hardware. Our system demonstrates the
feasibility of filtering raw voice input closer to users, in accordance with
their privacy preferences, while maintaining their authenticity.
