Secure multi-party computation (MPC) is a powerful cryptographic tool that allows users to perform privacy-preserving computations on their collective data, without revealing anything about their data to each other. For many decades MPC was considered impractical, but recent progress has made MPC significantly more practical to the point where it is attracting interest from government agencies as well as start-up companies.
I will survey the field of MPC, and then discuss my research aimed at making this technique more efficient, more robust, more scalable, and easier to use. In addition to new protocols, I will also describe EMP, an open-source toolkit I have developed that allows non-experts to apply MPC in real-world applications.
Xiao Wang is a postdoc researcher hosted by Vinod Vaikuntanathan at MIT and Ran Canetti at Boston University. He will join Northwestern University as an assistant professor in 2019 fall. Xiao Wang obtained his Ph.D. at the University of Maryland, where he was advised by Jonathan Katz.
His research interests include computer security, privacy, and cryptography, particularly on practical multi-party computation, oblivious RAM, and post-quantum cryptography. For this work, he has received a Best Paper Award in Applied Cyber Security at CSAW 2015, an iDASH Competition Award in 2015, a Human Longevity Inc. Award for MPC in 2016, and an ACM CCS Best Paper Award in 2017.