Bargav Jayaraman presented our paper on Evaluating Differentially Private Machine Learning in Practice at the 28th USENIX Security Symposium in Santa Clara, California.
Summary by Lea Kissner: Hey it's the results! pic.twitter.com/ru1FbkESho
— Lea Kissner (@LeaKissner) August 17, 2019
Also, great to see several UVA folks at the conference including:
Sam Havron (BSCS 2017, now a PhD student at Cornell) presented a paper on the work he and his colleagues have done on computer security for victims of intimate partner violence.
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.
Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal.
Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.