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.

Healthcare Data Analytics: Healthcare data and patient data can be securely and anonymously gathered from a large pool of users. Insights and analysis from the data can be computed using SMPC

Securely Training Machine Learning Models: Machine learning models can use privately-held data, while keeping the data’s integrity intact, to train themselves and capture insights from that data

Intelligent Vehicles and Swarm Robotics: SMPC could be instrumental in helping large-scale, real-time collaboration and anonymous communication between intelligent vehicles on the roads. Road vehicles can anonymously input data about road analytics and driving conditions in various conditions. The insights gleaned from this data could interest city planners, regulators, energy companies, emergency service providers, and car companies, among others. Similar principles could apply to swarm robotics, drone fleets, robot fleets, and ship fleets

Applications that will benefit from taking user data from a vast pool of users securely and privately and then computing it to find insights will benefit from Secure Multi-Party Computation.

Conclusion 

Secure Multi-Party Computation is an essential form of cryptographic computation and will be increasingly crucial in decentralized applications. Besides allowing anonymous collaboration, MPC will enable real data privacy and security. Secure Multi-Party Computation, used in conjunction with blockchain technology and other protocols such as Zero Knowledge Proofs, will open up a whole new range of possible use cases and allow people to anonymously and securely collaborate.