xaddh/multimodal-privacy
Auditing M-LLMs for Privacy Risks: A Synthetic Benchmark and Evaluation Framework Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources, including images and text. These resourceful data from social media also introduce a significant and underexplored privacy risk: the inference of sensitive personal attributes from seemingly daily media content. However, the lack of benchmarks… See the full description on the dataset page: https://huggingface.co/datasets/xaddh/multimodal-privacy.
| Author / organization | xaddh |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 481 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 28 May 2025 |
| Last updated at source | 9 November 2025 |
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Recent changes
- 17 Oct 2026 Description: Auditing M-LLMs for Privacy Risks: A Synthetic Benchmark and Evaluation Framework Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources, → Auditing M-LLMs for Privacy Risks: A Synthetic Benchmark and Evaluation Framework Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources,
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