Abstract: Companies such as Google or Lyft collect a substantial amount of location data about their users to provide useful services. The release of these datasets for general use can enable numerous innovative applications and research. However, such data contains sensitive information about the users, and simple clocking-based techniques have been shown to be ineffective to ensure users’ privacy. These privacy concerns have motivated many leading technology companies and researchers to develop algorithms that collect and analyze location data with formal provable privacy guarantees. I will show a unified framework that can (a) enhance a better understanding about the many existing provable privacy guarantees for location data; (b) allow flexible trade-offs between privacy, accuracy, and performance, based on the application’s requirements. I will also describe some exciting new research about provable privacy guarantees for handling advanced settings involving complex queries or datasets and emerging data-driven applications, and conclude with directions for future privacy research in big-data management and analysis.

Short 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 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.