1 citations · 1 across the 7 of their papers we have counts for
5 papers · 1 filter
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…