Video available here.

This event is co-sponsored by the Center for Statistics and Machine Learning.

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, banks wish to run joint anti-money laundering algorithms over their aggregate transaction data because criminals hide their traces across different banks.

To address such problems, Raluca and her students have designed cryptographic protocols and built efficient systems for secure collaborative learning, such as Delphi, Helen, MC^2, and Opaque. This talk will provide an overview of the work in this space, and then focus on one of the systems, Delphi, which enables secure collaborative inference for neural networks.

Bio:

Raluca Ada Popa is an assistant professor of computer science at University of California, 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 received her doctoral degree in computer science as well as her master’s and two bachelors’ degrees from Massachusetts Institute of Technology. She is the recipient of a Sloan Foundation Fellowship award, NSF Career, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT computer science doctoral thesis.