1 citations · 1 across the 7 of their papers we have counts for
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Trusted Hardware Acceleration for Malicious-Secure Function Secret Sharing
Yujie Xue, Yijing Peng, Lin Liu +5
Function secret sharing (FSS) underlies two-party private inference and private information retrieval, with cost dominated by generating, moving and evaluating distributed point fu…
KORD: Breaking the Key-Generation Bottleneck in Dealerless FSS via Protocol--Hardware Co-Design
Yijing Peng, Lin Liu, Yujie Xue +5
Function secret sharing (FSS) has become a core primitive in privacy-preserving computation. However, each FSS invocation requires a fresh pair of function keys generated by a trus…
Reconstructing Training Data from Adapter-based Federated Large Language Models
Silong Chen, Yuchuan Luo, Guilin Deng +4
Adapter-based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full-parameter fine-tuning for web-sc…
ENSI: Efficient Non-Interactive Secure Inference for Large Language Models
Zhiyu He, Maojiang Wang, Xinwen Gao +3
Secure inference enables privacy-preserving machine learning by leveraging cryptographic protocols that support computations on sensitive user data without exposing it. However, in…
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions
Wenhao Jiang, Yuchuan Luo, Guilin Deng +6
The integration of Large Language Models (LLMs) and Federated Learning (FL) presents a promising solution for joint training on distributed data while preserving privacy and addres…