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Abstract

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%. CCS CONCEPTS • Security and privacy Pseudonymity, anonymity and untraceability; Data anonymization and sanitization; • Human-centered computing Ubiquitous and mobile computing;

Information Disclosure in the Era of Voice Technology

“…We proposed that oral communication should increase perceived identifiability, consequently decreasing verbal disclosure propensity. We suggest that marketers interested in counteracting this deterrent of verbal disclosure may want to consider implementing techniques devised to "sanitize" consumers' voice input of its identifying characteristics in real time (e.g., Qian et al 2018). Such techniques entail altering consumers' voice locally (i.e., on the device), such that their voice print is no longer recognizable, while maintaining Smith 2018).…”

Configurable Privacy-Preserving Automatic Speech Recognition

“…speaker identity) while leaving linguistic content intact. Most of the proposed works focus on protecting/anonymizing speaker identity using voice conversion (VC) mechanisms [8,9,10]. These VC methods, however, aim to protect the speaker identity against different linkage attacks limited by the attacker's knowledge [11].…”

Privacy-preserving Voice Analysis via Disentangled Representations

“…In [61] Nautsch et al investigate the importance of the development of privacy-preserving technologies to protect speech signals and highlight the importance of applying these technologies to protect speakers and speech characterization in recordings. Some recent works have sought to protect speaker identity [67], gender identity [33] and emotion [2]. VoiceMask, for example, was proposed to mitigate the security and privacy risks of voice input on mobile devices by concealing voiceprints [67].…”

“…Some recent works have sought to protect speaker identity [67], gender identity [33] and emotion [2]. VoiceMask, for example, was proposed to mitigate the security and privacy risks of voice input on mobile devices by concealing voiceprints [67]. It aims to strengthen users' identity privacy by sanitizing the voice signal received from the microphone and then sending the perturbed speech to the voice input apps or the cloud.…”