Title: Preventing Privacy Leakage in Vision-Language Models: A Secure Framework for Large-Scale Image Classification
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Privacy-Masked Lables, Risk-Consistent, Classification

Abstract: Recently, large vision-language models (LVLMs) have demonstrated strong performance in generating pseudo-labels for diverse downstream tasks. However, during annotation or label generation, these models may inadvertently access sensitive information contained in the data (e.g., medical conditions, smoking habits), thereby creating potential risks of individual privacy leakage. To mitigate this challenge, we propose a novel framework that prevents LVLMs from accessing data associated with sensitive information. Specifically, our framework integrates a privacy label set with a randomized label set. Human annotators first determine whether the merged label set contains the ground-truth label; only when it does not, the LVLMs are employed to generate a pseudo-label. This mechanism ensures that LVLMs never directly access samples associated with sensitive information during annotation, while the inclusion of the randomized label set provides partial supervision for non-privacy samples. Moreover, we introduce a risk-consistent estimator that enables effective learning from LVLM-generated pseudo-labels under the exclusion of sensitive data. Extensive experiments on benchmark datasets demonstrate the superiority of our approach over state-of-the-art methods, effectively safeguarding sensitive label information while maintaining competitive model performance.