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Multi-P2A is a comprehensive benchmark dataset created by the Institute of Computing Technology, Chinese Academy of Sciences, intended to evaluate the privacy protection capabilities of large vision‑language models (LVLMs). The dataset covers 26 categories of personal privacy, 15 categories of commercial secrets, and 18 categories of state secrets, totaling 31,962 samples. It is constructed from existing datasets and social media platforms, generating samples via visual question answering (VQA) tasks to ensure high quality and diversity. Multi-P2A is mainly applied in privacy risk assessment, helping developers and researchers identify and mitigate potential privacy leaks in LVLMs during training and inference, thereby advancing privacy protection technologies.
