Secure collaborative learning using the MC^2 platform

Multiple organizations often wish to aggregate their sensitive data and learn from it, but they cannot do so because they cannot share their data.

For example, healthcare organizations wish to train models jointly over their aggregate patient data to discover better medical diagnosis or treatments, but they often cannot share this data due to privacy concerns or regulations.

To address such problems, my students and I developed MC^2, a framework for secure collaborative computation.

In this talk, I will describe MC^2 and the new possibilities it brings.

About the speaker

Raluca Ada Popa

Assistant Professor at UC Berkeley

Raluca Ada Popa is the Robert E. and Beverly A. Brooks assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography.

She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca has received her Ph.D. in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT.

She is the recipient of a Sloan Foundation Fellowship award, NSF Career, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, George M. Sprowls Award for best MIT CS doctoral thesis, and a Johnson award for best CS Masters of Engineering thesis from MIT.