The applications we use every day deal with privacy-sensitive data that come from different sources and entities, hence creating a tension between more functionality and privacy. Secure Multiparty Computation (MPC), a fundamental problem in cryptography and distributed computing, tries to resolve this tension by achieving the best of both worlds. But despite important and classic results, the practice of secure computation lags behind its theory by a wide margin.
In this talk, I discuss my work on a promising approach to making secure computation practical, namely server-aided MPC. This approach allows one to tap the resources of an untrusted cloud service to design more efficient and scalable privacy-preserving protocols. I discuss several variants of the server-aided model, our general- and special-purpose constructions for these variants, and the experimental results obtained form our implementations.
Payman Mohassel received his Ph.D. in Computer Science from University of California, Davis in 2009 (with Matthew Franklin). Since then he has held an assistant professor position at University of Calgary where he is currently employed. His research is in cryptography and information security with a focus on bridging the gap between the theory and practice of privacy-preserving computation.