  Recently, adapting the idea of self-supervised learning (SSL) on continuous
speech has started gaining attention. SSL models pre-trained on a huge amount
of unlabeled audio can generate general-purpose representations that benefit a
wide variety of speech processing tasks. Despite their ubiquitous deployment,
however, the potential privacy risks of these models have not been well
investigated. In this paper, we present the first privacy analysis on several
SSL speech models using Membership Inference Attacks (MIA) under black-box
access. The experiment results show that these pre-trained models are
vulnerable to MIA and prone to membership information leakage with high Area
Under the Curve (AUC) in both utterance-level and speaker-level. Furthermore,
we also conduct several ablation studies to understand the factors that
contribute to the success of MIA.
