Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective

no code implementations • 28 Feb 2024 • Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi

We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver's downstream models by manipulating the training data distribution of the diffusion model.

Fairness

Passive Inference Attacks on Split Learning via Adversarial Regularization

no code implementations • 16 Oct 2023 • Xiaochen Zhu, Xinjian Luo, Yuncheng Wu, Yangfan Jiang, Xiaokui Xiao, Beng Chin Ooi

SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL.

Federated Learning