A Secure Collaborative Learning Platform

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 train models jointly over their aggregate transaction data to detect money launderers because criminals hide their traces across different banks. To address such problems, my students and I developed MC^2, a framework for secure collaborative computation. My talk will overview our MC^2 platform, our technical approach, results, and adoption.

Bio: 

Raluca Ada Popa is an assistant professor of computer science at UC Berkeley. She is interested in security, systems, and applied cryptography. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others. Raluca received her PhD in computer science as well as her two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, a Google PhD Fellowship, a Johnson award for best CS Masters of Engineering thesis from MIT, and a CRA Outstanding undergraduate award from the ACM.

Open Data Science

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