Confidential Data Analytics and Learning for Data Scientists


Data scientists and analysts often wish to analyze confidential data, but these datasets are often locked down due to privacy concerns. Further, it is sometimes difficult to share confidential data between teams in the same organization or across organizations. In this talk, I will overview state-of-the-art techniques for protecting confidential data while _in use_. These methods encrypt the data while enabling data scientists to train models and run analytics queries on encrypted data, essentially ""sharing without showing"". I will then discuss our research and open source project called Opaque, which enables confidential analytics, learning and collaboration in an easy to use way. Link to open source project:


Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate 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 and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD 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 the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.

Open Data Science




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