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Privacy-preserving Voice Analysis via Disentangled Representations

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Document pages: 14 pages

Abstract: Voice User Interfaces (VUIs) are increasingly popular and built intosmartphones, home assistants, and Internet of Things (IoT) devices. Despiteoffering an always-on convenient user experience, VUIs raise new security andprivacy concerns for their users. In this paper, we focus on attributeinference attacks in the speech domain, demonstrating the potential for anattacker to accurately infer a target user s sensitive and private attributes(e.g. their emotion, sex, or health status) from deep acoustic models. Todefend against this class of attacks, we design, implement, and evaluate auser-configurable, privacy-aware framework for optimizing speech-related datasharing mechanisms. Our objective is to enable primary tasks such as speechrecognition and user identification, while removing sensitive attributes in theraw speech data before sharing it with a cloud service provider. We leveragedisentangled representation learning to explicitly learn independent factors inthe raw data. Based on a user s preferences, a supervision signal informs thefiltering out of invariant factors while retaining the factors reflected in theselected preference. Our experimental evaluation over five datasets shows thatthe proposed framework can effectively defend against attribute inferenceattacks by reducing their success rates to approximately that of guessing atrandom, while maintaining accuracy in excess of 99 for the tasks of interest.We conclude that negotiable privacy settings enabled by disentangledrepresentations can bring new opportunities for privacy-preservingapplications.

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