Privacy with Constraints: Challenges & Opportunities — Xi He
Abstract: Differential privacy has emerged as the state-of-the-art standard for privacy-preserving computation over databases containing sensitive information about individuals. Despite numerous and innovative research efforts over the last decade, there is a limited adoption of differential privacy by practitioners in industry or government agencies due to many challenges. In this talk, we will focus on one challenge brought by constraints — truthful knowledge about the databases known by adversaries. Designing private algorithms without taking these constraints into consideration may lead to attacks by adversaries with the knowledge of these constraints. We will show examples and applications on how to ensure the privacy under these constraints, and present open questions along this line.
Bio: Xi He is a 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 a double degree in Applied Mathematics and Computer Science from the University of Singapore. Xi has been working with Prof. Machanavajjhala on privacy since 2012. She has published in SIGMOD, VLDB, and CCS, and has given tutorials on privacy at VLDB 2016 and SIGMOD 2017. She received best demo award on differential privacy at VLDB 2016 and was awarded a 2017 Google Ph.D. Fellowship in Privacy and Security.