Secure multiparty computation (MPC) allows multiple owners of sensitive data to compute a joint function of that data, without revealing anything other than the function output. This enables entities to retain control of their private data even as while it is used. Consider, for instance, medical institutions (e.g. hospitals) each holding patient records that cannot be shared due to patient privacy regulations (GDPR laws). MPC makes it possible for these institutions to compute statistics that inform crucial medical research, all while preserving patient privacy.

Ivan Damgård, the head of the Aarhus crypto group, was one of the inventors of MPC back in the 1980’s [Read Here]. Since then, MPC has grown significantly, transitioning from theory to application. Its first real-world application was determining the market value of sugarbeets [Read Here]; recently, it was used to unveil the gender wage gap in Boston [Read Here]. 

Aarhus has been a hub of MPC research since its inception. We host the Theory and Practice of MPC workshop approximately every second year, and our researchers pioneer several key directions of MPC innovation. One such direction is using pre-processing to enhance the efficiency of MPC [Read Here]; another is designing protocols where every participant speaks at most once [Read Here] in order to boost scalability.