Synthesizing Relational Data with Differential Privacy
Providing access to sensitive data while preserving privacy is an important problem in the era of big data. A canonical solution to this problem is to replace the sensitive data with synthetic data that follow a similar distribution but do not reveal private information. In this talk, I will introduce our research efforts towards synthesizing relational data under differential privacy, which is a rigorous privacy framework with strong privacy guarantees. In particular, I will first present a method for synthesizing a single relational table, and then describe a more advanced technique for tackling the case of multiple tables with foreign key constraints. I will conclude the talk with directions for future work.
Xiaokui Xiao is a professor in the Department of Computer Science, National University of Singapore. His research focuses on data management, especially on data privacy and algorithms for large data. He is an IEEE fellow and ACM distinguished member. He received the 2021 VLDB Best Research Paper Award and the 2022 ACM SIGMOD Research Highlight Award. He obtained his Bachelor's and Master's degrees from the South China University of Technology, and his PhD from the Chinese University of Hong Kong.
Refreshments will be served after the talk