Summary of Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective, by Xinjian Luo et al.
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
by Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a new method for sharing pre-trained diffusion models across different organizations to improve data utilization and protect privacy. The authors highlight that while diffusion models have shown impressive generative performance, there is a lack of comprehensive examination of the potential risks associated with sharing these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sharing pre-trained diffusion models can be a game-changer for improving data utilization and protecting privacy by avoiding direct sharing of private data. However, this approach also raises some concerns that need to be explored further. The authors aim to fill this gap by investigating the potential risks and benefits of sharing these models. |
Keywords
* Artificial intelligence * Diffusion
