Revisiting Differentially Private Hyper-parameter Tuning
Zihang Xiang,
Tianhao Wang,
Chenglong Wang,
Di Wang,
Preprint
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[S&P'24] Preserving Node-level Privacy in Graph Neural Networks
Zihang Xiang,
Tianhao Wang,
Di Wang,
45th IEEE Symposium on Security and Privacy
CSAW'24 Applied Research Competition Finalist
[PDF]
[GitHub]
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[Science Advances] PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data
Juexiao Zhou,
Siyuan Chen,
Yulian Wu,
Haoyang Li,
Bin Zhang,
Longxi Zhou,
Yan Hu,
Zihang Xiang,
Zhongxiao Li,
Ningning Chen,
Wenkai Han,
Chencheng Xu,
Di Wang,
Xin Gao
Science Advances, VOLUME 10, ISSUE 5, 2 FEB 2024
[PDF]
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[S&P'23] A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction
Hanshen Xiao=,
Zihang Xiang=,
Di Wang,
Srini Devadas
44th IEEE Symposium on Security and Privacy
[PDF]
[GitHub]
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[SIGMOD'23] Practical Differentially Private and Byzantine-resilient Federated Learning
Zihang Xiang,
Tianhao Wang,
Wanyu Lin,
Di Wang
International Conference on Management of Data, 2023
[PDF]
[GitHub]
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[AISTATS'23] Privacy-preserving Sparse Generalized Eigenvalue Problem
Lijie Hu=,
Zihang Xiang=,
Jiabin Liu,
Di Wang
The 26th International Conference on Artificial Intelligence and Statistics, 2023
[PDF]
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[TKDE'23] Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem
Lijie Hu=,
Zihang Xiang=,
Jiabin Liu,
Di Wang
IEEE Transactions on Knowledge and Data Engineering, 2023
[PDF]
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Teaching
TA of CS229: Machine Learning, KAUST |
TA of BioE394E: Deep Learning for Bioengineering, KAUST |
TA of CS325: Private Data Analysis |
Service
(Sub)-Reviewer:
AISTATS'2025, CCS'(2024,2023), NDSS'2023, PETS'2023, ICML'2023, IEEE-TNNLS
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