Tianhao Wang is a data privacy and security researcher. He joined the University of Virginia School of Data Science as an Assistant Professor in 2022 with his primary appointment in the Department of Computer Science. He has extensive publications in top security and database conferences. His work about differentially private synthetic data generation won multiple awards in NIST’s competition. Prior to the University of Virginia, he obtained his Ph.D. from Purdue University in 2021 and held a postdoc position at Carnegie Mellon University.
Ph.D in Computer Science, Purdue University
B.Eng. in Software Engineering, Fudan University
Data Science Domains
Z. Li, T. Wang, and N. Li (2022). Differentially Private Vertical Federated Clustering. arXiv preprint, arXiv:2208.01700.
M. Zhou, T. Wang, T.H. Hubert Chan, G. Fanti, and E. Shi (2022). Locally differentially private sparse vector aggregation. 2022 IEEE Symposium on Security and Privacy, pp. 422-439.
F. Cicala, W. Wang, T. Wang, N. Li, E. Bertino, F. Liang, and Y. Yang (2021). Pure: A framework for analyzing proximity-based contact tracing protocols. ACM Computing Surveys, (CSUR) 55, no. 1, 1-36.
T. Wang, J. Qiongna Chen, Z. Zhang, D. Su, Y. Cheng, Z. Li, N. Li, and S. Jha (2021). Continuous release of data streams under both centralized and local differential privacy. 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 1237-1253.
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang (2021). When machine unlearning jeopardizes privacy. 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 896-911.
Z. Zhang, T. Wang, J. Honorio, N. Li, M. Backes, S. He, J. Chen, and Y. Zhang (2021). Privsyn: Differentially private data synthesis. arXiv:2012. 15128.
A. Xiong, T. Wang, N. Li, and S. Jha (2020). Towards effective differential privacy communication for users’ data sharing decision and comprehension. 2020 IEEE Symposium on Security and Privacy (SP), pp. 392-410.
T. Wang, B. Ding, M. Xu, Z. Huang, C. Hong, J. Zhou, N. Li, and S. Jha . Improving Utility and Security of the Shuffler-based Differential Privacy. Proceedings of the VLDB Endowment, Volume 13, Issue 13, pp. 3545-3558.
T. Wang, N. Li, and S. Jha (2018). Locally differentially private frequent itemset mining. 2018 IEEE Symposium on Security and Privacy (SP), pp. 127-143.
T. Wang, J. Blocki, N. Li, and S. Jha (2017). Locally differentially private protocols for frequency estimation. 26th USENIX Security Symposium (USENIX Security 17), pp. 729-745.
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