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Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
March 1, 2024, 5:42 a.m. | Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi
cs.LG updates on arXiv.org arxiv.org
Abstract: Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing privacy protection by avoiding sharing private data directly. However, the potential risks associated with such an approach have not been comprehensively examined.
In this paper, we take …
abstract academia adversarial arxiv attention coverage cs.ai cs.cr cs.lg diffusion diffusion models distribution fairness generative industry organizations performance perspective privacy proposals quality risks sampling terms type
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