Privacy for data exploration and machine learning; differential privacy; secure computation; data cleaning.
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.