Privacy-Preserving End-to-End Spoken Language Understanding
Yinggui Wang Wei Huang Le Yang
Spoken language understanding (SLU), one of the key enabling technologies for
human-computer interaction in IoT devices, provides an easy-to-use user
interface. Human speech can contain a lot of user-sensitive information, such
as gender, identity, and sensitive content. New types of security and privacy
breaches have thus emerged. Users do not want to expose their personal
sensitive information to malicious attacks by untrusted third parties. Thus,
the SLU system needs to ensure that a potential malicious attacker cannot
deduce the sensitive attributes of the users, while it should avoid greatly
compromising the SLU accuracy. To address the above challenge, this paper
proposes a novel SLU multi-task privacy-preserving model to prevent both the
speech recognition (ASR) and identity recognition (IR) attacks. The model uses
the hidden layer separation technique so that SLU information is distributed
only in a specific portion of the hidden layer, and the other two types of
information are removed to obtain a privacy-secure hidden layer. In order to
achieve good balance between efficiency and privacy, we introduce a new
mechanism of model pre-training, namely joint adversarial training, to further
enhance the user privacy. Experiments over two SLU datasets show that the
proposed method can reduce the accuracy of both the ASR and IR attacks close to
that of a random guess, while leaving the SLU performance largely unaffected.