2 papers
cs.CR2025
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
Wenhan Dong, Chao Lin, Xinlei He +2
Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or…
cs.CR2024
Towards Understanding and Enhancing Security of Proof-of-Training for DNN Model Ownership Verification
Yijia Chang, Hanrui Jiang, Chao Lin +2
The great economic values of deep neural networks (DNNs) urge AI enterprises to protect their intellectual property (IP) for these models. Recently, proof-of-training (PoT) has bee…