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 infeasible, 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 Ph.D. student at the University of Maryland, where he is advised by Professor Jonathan Katz. His research interests focus on applied cryptography, specifically on designing efficient privacy-preserving systems based on secure multi-party computation. 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.