Xi He
Appointment
Canada CIFAR AI Chair
National Program Committee member
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2022
Xi He is a Canada CIFAR AI Chair at the Vector Institute. Her research focuses on the areas of privacy and security for big data, including the development of usable and trustworthy tools for data exploration and machine learning with provable security and privacy (S&P) guarantees.
Rather than patching systems for their S&P issues, her work takes a principled approach to designing provable S&P requirements and building practical tools that achieve these requirements. Considering S&P as a first-class citizen in system and algorithm design, she has demonstrated new optimization opportunities for these S&P-aware database systems and machine learning tools.
Xi He has published in the top database, privacy, and ML conferences including SIGMOD, VLDB, CCS, PoPets, AAAI, and presented highly regarded tutorials on privacy at VLDB 2016, SIGMOD 2017, and SIGMOD 2021. Her book “Differential Privacy for Databases,” co-authored by Joseph Near, was published in 2021.
Awards
- Faculty of Mathematics Golden Jubilee Research Excellence Award, Faculty of Mathematics, University of Waterloo, 2024
- Data Analytics and Insights (DANI) Award, Google, 2023
- Meta Privacy-preserving Technologies Research Award, Meta, 2022
- Distinguished PC, ACM SIGMOD, 2021
- Outstanding Ph.D. Dissertation Award, Duke University, 2018
- Google PhD Fellowship in Privacy and Security, 2017
- Best Demo Award, International Conference on Very Large Data Bases, 2016
Relevant Publications
- Mohapatra, S., Zong, J., Kerschbaum, F., & He, X. (2024). Differentially private data generation with missing data. Proceedings of the VLDB Endowment.
- Pappachan, P., Zhang, S., He, X., & Mehrotra, S. (2024). Preventing inferences through data dependencies on sensitive data. IEEE Transactions on Knowledge and Data Engineering (TKDE).
- Zhang, S., He, X., Kundu, A., Mehrotra, S., & Sharma, S. (2024). Secure normal form: Mediation among cross cryptographic leakages in encrypted databases. In Proceedings of the 40th International Conference on Data Engineering.
- Pappachan, P., Zhang, S., He, X., & Mehrotra, S. (2023). Preventing inferences through data dependencies on sensitive data. IEEE Transactions on Knowledge and Data Engineering.
- Mohapatra, S., Sasy, S., He, X., Kamath, G., & Thakkar, O. (2022). The role of adaptive optimizers for honest private hyperparameter selection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7806-7813.