Membership Inference Attacks Against Self-supervised Speech Models
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.
Code (1)
Tasks
Self-Supervised LearningSimilar Papers 제목 키워드 기반
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
Semi-supervised learning (SSL) leverages both labeled and unlabeled data to train machine learning (ML) models. State-of-the-art SSL methods can achieve comparable performance to supervised learning by leveraging much fe…
Data AugmentationInference AttackMembership Inference AttackMitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leakage in machine learning (ML) models. These attacks aim to distinguish training members from non-members by exploiting differential behavior of the mo…
Privacy PreservingA Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability
Self-supervised learning shows promise in harnessing extensive unlabeled data, but it also confronts significant privacy concerns, especially in vision. In this paper, we aim to perform membership inference on visual sel…
Contrastive LearningSelf-Supervised LearningEffects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three …
Inference AttackMembership Inference AttackReconstruction and Membership Inference Attacks against Generative Models
We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative m…
Density EstimationInference AttackMembership Inference Attack