Zihang Xiang

I am a 4th-year Ph.D. student at King Abdullah University of Science and Technology (KAUST), advised by Di Wang. I also work closely with Tianhao Wang at University of Virginia. I earned my Bachelor's and Master's degree in Electrical Engineering at Shanghai Jiao Tong University (SJTU) IEEE Pilot Class . I worked on power electronics (specialized in hardware design and control theory) during that time.

I will be joining Prof. Yuan Tian's group as a postdoc at UCLA in 2025's summer.

My current research is on privacy-preserving data analysis. I am interested in pushing the boundaries of differential privacy via principled approaches in broad machine learning applications.

Email  /  Google Scholar

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Publications [* means equal contribution ]


USENIX Sec'25
Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run
Zihang Xiang, Tianhao Wang, Di Wang
34th USENIX Security Symposium
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
Science Advances
A privacy-preserving federated machine learning method protects patients' privacy in omic data
Juexiao Zhou, ..., Zihang Xiang, ..., Di Wang, Xin Gao
Science Advances, VOLUME 10, ISSUE 5, 2 FEB 2024
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
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
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
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

Teaching


TA of CS229: Machine Learning, KAUST
TA of BioE394E: Deep Learning for Bioengineering, KAUST
TA of CS325: Private Data Analysis, KAUST
TA of CS294: Trustworthy Machine Learning, KAUST

Service


(Sub)-Reviewer: AISTATS'2025, CCS'(2024,2023), NDSS'2023, PETS'2023, ICML'2023, IEEE-TNNLS