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.
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.