An Opt-in Framework for Privacy Protection in Audio-Based Applications

Abstract

Installing audio-based applications exposes users to the risk of the data processor extracting additional information beyond the task the user permitted. To solve these privacy concerns, we propose to integrate an on-edge data obfuscation between the audio sensor and the recognition algorithm. We introduce a novel privacy loss metric and use adversarial learning to train an obfuscator. Contrary to existing work, our technique does not require users to specify which sensitive attributes they want to protect (opt-out) but instead only provide permission for specific tasks (opt-in). Moreover, we do not require retraining of recognition algorithms, making the obfuscated data compatible with existing methods. We experimentally validate our approach on four voice datasets and show that we can protect several attributes of the speaker, including gender, identity, and emotional state with a minimal recognition accuracy degradation.