Abstract: Data gathering is easier and more pervasive today than in any previous point in history. While the accelerating growth of data has empowered many research and real-world applications, recent incidents of data leakage and abuse have raised public concerns for privacy. Differential privacy provides a promising way to release analysis on sensitive data in a privacy-preserving manner. It has become mainstream in many research communities and has been deployed in practice in the private sector and some government agencies. However, designing differentially private algorithms is notoriously difficult and error prone. Significant errors happen even in peer-reviewed papers and systems. Also, asking for a little more privacy will come at a cost–often to the accuracy of some analysis. Balancing individuals' privacy and data utility has become a vital yet challenging task for both researchers and data analysts. In this talk, I will present our work on automatically checking for the correctness of differentially private algorithms and optimizing for their privacy/utility tradeoffs. Our work facilitates the adoption of differential privacy by increasing its utility and making it easier to deploy for non-experts.

Short Bio: Zeyu Ding is a doctoral candidate in Computer Science at Penn State University advised by Prof. Daniel Kifer and Prof. Danfeng Zhang. His research interests lie in the intersection of security, privacy, formal methods and machine learning. He obtained a doctoral degree in Mathematics from SUNY Binghamton in 2015. His work has been published at top venues and has won one best paper award (2018), two best paper runner-up awards (2020, 2021) and the Caper Bowden PET Award Runner-up (2019). He also received a research award in 2019 and a teaching award in 2021 from Penn State University.