1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach
Guilin Deng, Silong Chen, Yuchuan Luo +6
Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine-tune LLMs without sharing raw data, addressing challenges of limited resources and privacy…
LEA: Label Enumeration Attack in Vertical Federated Learning
Wenhao Jiang, Shaojing Fu, Yuchuan Luo +1
A typical Vertical Federated Learning (VFL) scenario involves several participants collaboratively training a machine learning model, where each party has different features for th…
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…
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
Huali Xu, Li Liu, Shuaifeng Zhi +4
Existing Cross-Domain Few-Shot Learning (CDFSL) methods require access to source domain data to train a model in the pre-training phase. However, due to increasing concerns about d…