Dr. Xi He, The University of Waterloo

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Abstract:Computing technology has enabled massive digital traces of our personal lives to be collected and stored. These datasets play an important role in numerous real-life applications and research analysis, such as contact tracing for COVID 19, but they contain sensitive information about individuals. When managing these datasets, privacy is usually addressed as an afterthought, engineered on top of a database system optimized for performance and usability. This has led to a plethora of unexpected privacy attacks in the news. Specialized privacy-preserving solutions usually require a group of privacy experts and they are not directly transferable to other domains. There is an urgent need for a general trustworthy database system that offers end-to-end privacy guarantees. This talk will present the challenges in designing such a system and highlight our efforts to make the system efficient, robust, and usable for database clients while achieving provable privacy guarantees.  

Biography: Dr. Xi He is currently an Assistant Professor in the Cheriton School of Computer Science at the University of Waterloo. Her research interests span the areas of privacy and security for big-data management and analysis. She completed her Ph.D. under Dr. Ashwin Machanavajjhala in the Computer Science Department of Duke University. She also received a double degree in Applied Mathematics and Computer Science from the University of Singapore. She has published in SIGMOD, VLDB, CCS, and has given tutorials on privacy at VLDB 2016 and SIGMOD 2017. She received a best demo award on differential privacy at VLDB 2016 and was awarded a 2017 Google Ph.D. Fellowship in Privacy and Security.