Our research seeks to empower individuals and organizations to
control how their data is used. We use techniques from cryptography,
programming languages, machine learning, operating systems, and other
areas to both understand and improve the security of computing as
practiced today, and as envisioned in the future.
Everyone is welcome at our research group meetings
(most Fridays at 11am, but join the slack group for announcements). To
get announcements, join our Slack Group (any
@virginia.edu email address can join themsleves, or email me
to request an invitation).
I’m quoted in this article by Will Knight focused on the work Oasis Labs (Dawn Song’s company) is doing on privacy-preserving medical data analysis: How AI could save lives without spilling medical secrets, MIT Technology Review, 14 May 2019.
“The whole notion of doing computation while keeping data secret is an incredibly powerful one,” says David Evans, who specializes in machine learning and security at the University of Virginia. When applied across hospitals and patient populations, for instance, machine learning might unlock completely new ways of tying disease to genomics, test results, and other patient information.
“You would love it if a medical researcher could learn on everyone’s medical records,” Evans says. “You could do an analysis and tell if a drug is working on not. But you can’t do that today.”
Despite the potential Oasis represents, Evans is cautious. Storing data in secure hardware creates a potential point of failure, he notes. If the company that makes the hardware is compromised, then all the data handled this way will also be vulnerable. Blockchains are relatively unproven, he adds.
“There’s a lot of different tech coming together,” he says of Oasis’s approach. “Some is mature, and some is cutting-edge and has challenges.”
(I’m pretty sure I didn’t actually say “tech” in my call with Will
Knight since I wouldn’t use that wording, but would say
Xiao Zhang and Saeed Mahloujifar will present our work on Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness at two workshops May 6 at ICLR 2019 in New Orleans: Debugging Machine Learning Models and Safe Machine Learning:
Specification, Robustness and Assurance.
Some photos for our lunch to celebrate the end of semester, beginning
of summer, and congratulate Weilin Xu on his PhD:
Left to right
: Jonah Weissman, Yonghwi Kown, Bargav Jayaraman, Aihua Chen, Hannah Chen, Weilin Xu, Riley Spahn, David Evans, Fnu Suya, Yuan Tian, Mainuddin Jonas, Tu Le, Faysal Hossain, Xiao Zhang, Jack Verrier
I had the privilege of speaking at the JASON Spring Meeting,
undoubtably one of the most diverse meetings I’ve been part of with
talks on hypersonic signatures (from my DSSG 2008-2009 colleague, Ian
Boyd), FBI DNA, nuclear proliferation in Iran, engineering biological
materials, and the 2020 census (including a very interesting
presentatino from John Abowd on the differential privacy mechanisms
they have developed and evaluated). (Unfortunately, my lack of
security clearance kept me out of the SCIF used for the talks on
quantum computing and more sensitive topics).
Slides for my talk: [PDF]