Xi He’s 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, Xi’s 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. She has published in the top database, privacy, and ML conferences, including SIGMOD, VLDB, CCS, PoPets, AAAI, a book on “Differential Privacy for Databases” in Foundations and Trends in Databases, and presented highly regarded tutorials on privacy at VLDB 2016, SIGMOD 2017, and SIGMOD 2021.
Faculty Member
Assistant Professor, David R. Cheriton School of Computer Science, University of Waterloo
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Privacy for data exploration and machine learning
- Differential privacy
- Secure computation
- Data cleaning
Publications
Visualizing privacy-utility trade-offs in differentially private data releases
2022
The role of adaptive optimizers for honest private hyperparameter selection
2022
Cache me if you can: Accuracy-aware inference engine for differentially private data exploration
2022
Don’t be a tattle-tale: preventing leakages through data dependencies on access control protected data
2022
MIDE: accuracy aware minimally invasive data exploration for decision support
2022
Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy
2022