|Jianwei Qian||Illinois Institute of Technology|
|Haohua Du||Illinois Institute of Technology|
|Jiahui Hou||Illinois Institute of Technology|
|Linlin Chen||Illinois Institute of Technology|
|Taeho Jung||University of Notre Dame|
|Xiang-Yang Li||University of Science and Technology of China|
To address privacy issues in speech recognition, the authors propose to add an intermediary between users and the cloud, named VoiceMask, to anonymize speech data before sending it to the cloud for speech recognition.
We are speeding toward a not-too-distant future when we can perform human-computer interaction using solely our voice. Speech recognition is the key technology that powers voice input, and it is usually outsourced to the cloud for the best performance. However, user privacy is at risk because voiceprints are directly exposed to the cloud, which gives rise to security issues such as spoof attacks on speaker authentication systems. Additionally, it may cause privacy issues as well, for instance, the speech content could be abused for user profiling. To address this unexplored problem, we propose to add an intermediary between users and the cloud, named VoiceMask, to anonymize speech data before sending it to the cloud for speech recognition. It aims to mitigate the security and privacy risks by concealing voiceprints from the cloud. VoiceMask is built upon voice conversion but is much more than that; it is resistant to two de-anonymization attacks and satisfies differential privacy. It performs anonymization in resource-limited mobile devices while still maintaining the usability of the cloud-based voice input service. We implement VoiceMask on Android and present extensive experimental results. The evaluation substantiates the efficacy of VoiceMask, e.g., it is able to reduce the chance of a user's voice being identified from 50 people by a mean of 84%, while reducing voice input accuracy no more than 14.2%.