An Opt-in Framework for Privacy Protection in Audio-Based Applications
To address the privacy risks of audio applications extracting unauthorized user data, this work proposes an on-edge data obfuscator trained through adversarial learning, which uniquely operates on an opt-in permission model to protect sensitive speaker attributes while maintaining compatibility with existing recognition algorithms and incurring minimal accuracy degradation.
Oct 1, 2022
