There is a recent surge of interest in performing computations on encrypted data. Techniques such as secure multi-party computations and fully-homomorphic encryption enable rich privacy-preserving applications. On the other hand, building such applications is hard due to the cryptographic expertise required to build them correctly, securely, and
efficiently. Excitingly, a compiler that raises the level of programming abstraction while performing a slew of domain-specific optimizations can make a huge difference in building these applications. I will provide an overview of recent work in this area while focusing
on our own work on a compiler for fully-homomorphic computations. Here the fundamental problem reduces to mapping application level parallelism onto the vectorization capabilities inherent in the encryption schemes.
Madan Musuvathi manages the Research in Software Engineering (RiSE) group at Microsoft
Research. His interests primarily lie in the intersection of programming languages, formal
methods, and systems. His research has produced several software reliability tools that
are widely used within Microsoft and other companies. He received his Ph.D. from Stanford
Lecture online (YouTube)