Position: PhD student Final year
Current Institution: Duke University
Differential Privacy in the Wild: current practices & open challenges
Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. In this talk, we will cover the state of the art techniques in differentially private computation on tabular data, highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications
Xi He is a fourth year Ph.D. student at Computer Science Department, Duke University. Her research interests lie in privacy-preserving data analysis and security. She has also received an M.S from Duke University and double degree in Applied Mathematics and Computer Science from University of Singapore. Xi has been working with Prof. Machanavajjhala on differential privacy since 2012, and has published several work in SIGMOD and VLDB. Her research work on ‘Differential Privacy Trajectories Synthesis’ has been awarded as ‘the Outstanding Ph.D. Research Initial Project Award’ by Duke CS Department in 2014. She has been selected as a member of the U.S. delegation to the 2nd Heidelberg Laureate Forum. As a female CS researcher, she is also a recipient of Grace Hopper Conference Scholarship Grant in 2014 and lead Duke ACM-W chapter as president in 2015-2016.