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cs.CR2025
EASE: Practical and Efficient Safety Alignment for Small Language Models
Haonan Shi, Guoli Wang, Tu Ouyang +1
Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct…
cs.CR2025
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
Haonan Shi, Tu Ouyang, An Wang
Federated Distillation (FD) has emerged as a popular federated training framework, enabling clients to collaboratively train models without sharing private data. Public Dataset-Ass…
cs.CR2024
Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks
Haonan Shi, Tu Ouyang, An Wang
Machine learning models, in particular deep neural networks, are currently an integral part of various applications, from healthcare to finance. However, using sensitive data to tr…