Towards Privacy-Preserving Audio Classification Systems
arxiv(2024)
摘要
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.
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