Secure Multi-Party Computation Use Cases

Secure Multi-Party Computation (SMPC), as described by Wikipedia, is a subset of cryptography to create methods for multiple users to jointly compute a function over their inputs while keeping those inputs private. A significant benefit of Secure Multi-Party Computation is that it preserves data privacy while making it usable and open for analysis. I've explained how SecureMulti-Party Computation and Fair Multi-Party Computation work in earlier posts. While there are several emerging Use Cases of Secure Multi-Party Computation, I'm going to focus on three use cases in this post: autonomous vehicles and swarm robotics, healthcare data and analytics, and lastly, securely training machine learning models. Below are three use cases that would benefit from Secure Multi-Party Computation, i.e., being able to jointly compute a function over their inputs while keeping those inputs private.