8 citations · 12 across the 4 of their papers we have counts for
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cs.CR2024★ 1 cited
Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
Boheng Li, Yishuo Cai, Haowei Li +3
Model quantization is widely used to compress and accelerate deep neural networks. However, recent studies have revealed the feasibility of weaponizing model quantization via impla…
cs.CR2023★ 2 cited
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
Hongwei Yao, Zheng Li, Haiqin Weng +3
Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent…
cs.CR2020★ 8 cited
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…