Towards a secure collaborative learning platform
Raluca Ada Popa
Abstract:
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, my students and I have designed cryptographic protocols and built efficient systems for secure collaborative learning, such as Delphi, Helen, MC^2, and Opaque. I will overview our work in this space, and then focus on one of our systems, Helen, which enables collaborative training of linear models that is secure in a demanding malicious setting.
Bio:
Raluca Ada Popa is an 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 PhD in computer science as well as her Masters and two BS degrees 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.