Towards Privacy-Preserving Audio Classification Systems

TL;DR

This paper discusses privacy concerns in audio classification systems and proposes privacy-preserving audio features to protect user data while enabling effective classification.

Contribution

It introduces novel privacy-preserving audio features designed to classify audio without compromising user privacy.

Findings

Proposed audio features maintain classification accuracy.

Addressed ethical concerns in audio data collection.

Outlined research directions for privacy-preserving audio sensing.

Abstract

Audio signals can reveal intimate details about a person's life, including their conversations, health status, emotions, location, and personal preferences. Unauthorized access or misuse of this information can have profound personal and social implications. In an era increasingly populated by devices capable of audio recording, safeguarding user privacy is a critical obligation. This work studies the ethical and privacy concerns in current audio classification systems. We discuss the challenges and research directions in designing privacy-preserving audio sensing systems. We propose privacy-preserving audio features that can be used to classify wide range of audio classes, while being privacy preserving.